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Financial advisers believe increased demand for individual protection products will come from parents with young families, according to new independent research for HSBC Life UK.
Over half (53 percent) of advisers surveyed highlighted parents with young children will have the biggest need for protection, while parents starting a family will also potentially benefit from individual protection, 43 percent of financial advisers revealed.
According to the Association of British Insurers, a significant number of households are under-protected individually. Of the 26.5 million UK households in 2018, 5.4 million had paid towards life insurance but just 0.2 million had some form of income protection.
The research found that it isn’t just families that could benefit from protection. Over two fifths (45 percent) of advisers believe employees with limited access to employee benefits through their current employer are another demographic that could experience a need for individual protection products.
The self-employed (39 percent), small business owners (37 percent) and gig economy workers (32 percent) will also have a protection requirement, likely to be due to a lack of access to employment benefits like sick pay, holiday pay and redundancy pay. With nearly 4.7 million workers classified as self-employed and 2.8 million estimated to work in the gig economy, it is understandable that these segments are seen to be driving demand. During the coronavirus pandemic, this segment of the workforce has been hit hard, with the government’s Self-Employment Income Support Scheme only available to those with profits below £50,000 and not available to anyone who has become self-employed in the last tax year.
Mark Hussein, CEO of HSBC Life UK, said: “There is increasing awareness that protection cover is an important investment, both in the short and long term. It is often the case that having children means people think more about the future and considers whether they and their families are appropriately financially protected, should anything happen.
“It is interesting that advisers expect to experience demand from self-employed and gig economy workers. These groups rely on regular work and are unlikely to have the same level of protection offered as those individuals working for an employer offering benefits such as critical illness cover or death in service, so it’s vital that they protect their income. Advisers have an important role to play in helping customers of all demographics to understand the need for greater individual protection.”
One in eight (13 percent) of advisers believe those still working and over the age of 50 could also require individual protection products. This is an age group likely to have parents still alive but starting to need care and support, plus they will often financially support children, so will perhaps be more focused on protecting their family than other age groups.
The table below shows a breakdown of the type of customers advisers believe will see increased demand for protection products in the coming years.

Financial advisers believe increased demand for individual protection products will come from parents with young families, according to new independent research for HSBC Life UK. Over half (53 percent) of advisers surveyed highlighted parents with young children will have the biggest need for protection, while parents starting a family will also potentially benefit from individual […] More

Advisers Survey Shows Demand for Life/Health Cover Has Increased

Flying Eze

No, neither do we here at IE magazine. It is a confusing and rapidly evolving sector of the global finance and monetary system, but there is demand for asset protection of course. Now Evertas, the world’s first cryptoasset insurance company, says there is a serious problem of ‘phantom’ knowledge of cryptoassets from insurers.
The company has been engaging with several insurers and brokers on the issue of insuring cryptoassets, and has found many wrongly claim to have a good understanding of the risks associated with these assets, and mistakenly believe that the sector’s underwriting and pricing in this area are strong.
Evertas argues that with a global capacity of just $1 billion to $2 billion from insurers for a market that is worth over $250 billion, and with no dedicated underwriting or claims teams, they hugely overestimate their level of understanding of cryptoassets and the quality of service they provide to this market.
New research from Evertas amongst senior insurance executives reveals that 68% believe the quality of underwriting of cryptoassets within the industry is good, with just 32% describing it as average or poor.
Similarly, 76% believe the pricing of insurance cover for cryptoassets is strong, and 42% say the claims process is good or excellent.
J Gdanski, CEO and Founder of Evertas, said: “Cryptoassets and blockchain systems involve multiple disparate technologies and a range of risks including cyber, regulatory and fraud that are unlike any other market.  Although our findings reveal that senior insurance executives believe the sector has a good understanding of the risks around cryptoassets and that underwriting, pricing and claims are effective, the reality is they are not.  Proof of this can be found in the market’s woeful lack of capacity for the cryptoasset and blockchain markets.
“However, there is little doubt that the market capitalisation of cryptoassets is growing rapidly and that it represents an exciting opportunity for insurers and brokers. Their challenge is to build the knowledge and expertise needed in-house – although this can be expensive and take a long time – or partner with specialists in this area like us.”
Evertas believes it’s the only company in the world focused solely on providing insurance coverage for cryptoassets and users of blockchain systems.  It has a unique offering providing a full suite of services for these assets – from risk audits, to underwriting, investigations and claims handling.
Founded in 2017, Evertas has a world-leading team led by an experienced group of executives including the founding Chief Insurance Officer (and founding member) of Lemonade, a blockchain team leader at Bank of New York, and a blockchain industry insurance expert. The founders have over 40 years of combined experience in the insurance sector, and more than ten in the crypto and blockchain markets.
Earlier this year, Evertas received its licence from the Bermuda Monetary Authority to start operating from the jurisdiction.  It operates as a ‘Class 3A’ insurer.

No, neither do we here at IE magazine. It is a confusing and rapidly evolving sector of the global finance and monetary system, but there is demand for asset protection of course. Now Evertas, the world’s first cryptoasset insurance company, says there is a serious problem of ‘phantom’ knowledge of cryptoassets from insurers. The company […] More

Quick Question; Do You Know How to Underwrite Cryptoassets Risk?

Insurance Edge is always interested in new ideas, especially when those techniques have been studied and assessed in a real world scenario, via the Insurance Fraud Bureau. In this article Dr Tim Mitchell and Dr Benny Cheung take a look at fraud and how patterns of human behaviour can be modified. Prevention can sometimes be better than cure.
Author Biographies
Dr Tim Mitchell is a Principal at Dectech, having joined in 2007. He received his PhD from Brunel University following degrees in Psychology (BSc) and Computer Science (MSc) at the University of Bristol and Bath University respectively. At Decision Technology he runs behavioural research projects for clients from a variety of industries including finance, telecommunications, retail and utilities.
Dr Benny Cheung is a Director at Dectech, where he has been since 2005. Prior to joining Decision Technology, Benny completed a PhD and a two-year research fellowship in behavioural science at the University of Cambridge. His areas of commercial expertise include retail, energy, financial services, advertising and telecommunications. He also heads the firm’s Brand Practice and personally oversees all related client accounts and internal R&amp;D initiatives.
<img class="size-full wp-image-13066 aligncenter" src="https://i0.wp.com/insurance-edge.net/wp-content/uploads/2020/02/crime-uk-insurance-fraud.jpg?resize=586%2C331&amp;ssl=1" sizes="(max-width: 586px) 100vw, 586px" srcset="https://i0.wp.com/insurance-edge.net/wp-content/uploads/2020/02/crime-uk-insurance-fraud.jpg?w=586&amp;ssl=1 586w, https://i0.wp.com/insurance-edge.net/wp-content/uploads/2020/02/crime-uk-insurance-fraud.jpg?resize=300%2C169&amp;ssl=1 300w, https://i0.wp.com/insurance-edge.net/wp-content/uploads/2020/02/crime-uk-insurance-fraud.jpg?resize=174%2C98&amp;ssl=1 174w" alt="crime-uk-insurance-fraud" width="586" height="331" data-attachment-id="13066" data-permalink="https://insurance-edge.net/2020/02/18/how-risky-is-your-city-riskline-has-the-answer-for-business-travellers/crime-uk-insurance-fraud-2/" data-orig-file="https://i0.wp.com/insurance-edge.net/wp-content/uploads/2020/02/crime-uk-insurance-fraud.jpg?fit=586%2C331&amp;ssl=1" data-orig-size="586,331" data-comments-opened="1" data-image-meta="{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;1&quot;}" data-image-title="crime-uk-insurance-fraud" data-image-description="" data-medium-file="https://i0.wp.com/insurance-edge.net/wp-content/uploads/2020/02/crime-uk-insurance-fraud.jpg?fit=300%2C169&amp;ssl=1" data-large-file="https://i0.wp.com/insurance-edge.net/wp-content/uploads/2020/02/crime-uk-insurance-fraud.jpg?fit=586%2C331&amp;ssl=1" data-recalc-dims="1" />
How Can Insurers Help to Prevent Opportunistic Fraud Attempts?
Opportunistic insurance fraud occurs when otherwise law-abiding citizens are dishonest or exaggerate when making insurance applications or claims in order to get a lower premium or higher claim. It is estimated to cost the insurance industry in the UK around £1 billion every single year 1. For many insurers it is therefore a multi-million-pound problem, and one which causes the premiums of honest customers to be higher than they otherwise would be. In turn, it leads to greater customer dissatisfaction and an erosion of trust between customers and insurers.
Opportunistic fraud differs from fraud perpetrated by organised criminal gangs, but is no less a headache for insurers to deal with. It will come as no surprise, then, that insurers put significant investment into tackling fraud. This investment typically goes into the detection of possible fraud, broadly with three key steps involved 2:
· Detection. This is typically automated with algorithms in place determined from approaches such as machine learning. These do the ‘heavy lifting’ of sifting through the vast volumes of applications, claims and other data to flag anomalies signalling potential fraud, which may then be investigated by a person.
· Investigation. Claims or applications flagged as potentially fraudulent are then typically investigated by a person. This step helps to avoid instances where an application or claim is legitimate and has incorrectly been flagged by the automated system.
· Resolution. Once a suspected case of fraud has been detected and investigated, the final step is for the case to be resolved, which in some cases can mean criminal prosecution. Sufficient evidence is needed for this step to take place, and it is inevitably a time-consuming process.
<h2>The issues with detection</h2>
Fraud detection and the steps that follow it are an important process in the battle against insurance fraud, but are not without their issues, and more needs to be done 3. The process of detection is not a simple one, with the following downsides:
· Implementation Complexity. Putting in place the various means of detecting fraud is not trivial. From implementing AI and machine learning techniques, to cross-provider data sharing, there are a number of processes that need to fit together seemlessly.
· Time-consuming. As well as having automated processes in places, detection of fraud will typically involve the downstream processes of human investigation and prosecution, mentioned above, in order to be successful. From flagging a potential case of fraud to successful resolution can take time.
· Cost. The process of detection and downstream investigation and prosecution is therefore costly to set up and maintain.
· Reputation Risks. Investigating potentially fraudulent applications and claims can mean interruptions to the service given to the customers in question. Particularly in instances where the application or claim proves to not be fraudulent, this presents some reputation risk to the insurer, in turn harming potential future sales.
There are difficulties, then, in detecting and tackling insurance fraud, and opportunistic fraud is especially hard to detect. This is because the spur-of-the-moment nature of opportunistic fraud (as opposed to the premeditated nature of organised criminal fraud) leaves few clues to the fraud taking place. However, the spur-of-the-moment nature of opportunistic fraud also makes it susceptible to behavioural ‘nudges’, which raises the prospect of whether customers can be influenced to behave more honestly. The insurance industry needs a new weapon in the battle against opportunistic fraud – is prevention the answer?
<img class="wp-image-10229 aligncenter" src="https://i1.wp.com/insurance-edge.net/wp-content/uploads/2019/10/claimed-and-shamed-features-CEGA-fraud-tips-and-advice.jpg?resize=562%2C316&amp;ssl=1" sizes="(max-width: 562px) 100vw, 562px" srcset="https://i1.wp.com/insurance-edge.net/wp-content/uploads/2019/10/claimed-and-shamed-features-CEGA-fraud-tips-and-advice.jpg?w=624&amp;ssl=1 624w, https://i1.wp.com/insurance-edge.net/wp-content/uploads/2019/10/claimed-and-shamed-features-CEGA-fraud-tips-and-advice.jpg?resize=300%2C169&amp;ssl=1 300w, https://i1.wp.com/insurance-edge.net/wp-content/uploads/2019/10/claimed-and-shamed-features-CEGA-fraud-tips-and-advice.jpg?resize=580%2C326&amp;ssl=1 580w, https://i1.wp.com/insurance-edge.net/wp-content/uploads/2019/10/claimed-and-shamed-features-CEGA-fraud-tips-and-advice.jpg?resize=174%2C98&amp;ssl=1 174w" alt="claimed and shamed features CEGA fraud tips and advice" width="562" height="316" data-attachment-id="10229" data-permalink="https://insurance-edge.net/2019/10/19/cega-anti-fraud-tips-to-be-showcased-on-bbcs-claimed-shamed-show/claimed-and-shamed-features-cega-fraud-tips-and-advice/" data-orig-file="https://i1.wp.com/insurance-edge.net/wp-content/uploads/2019/10/claimed-and-shamed-features-CEGA-fraud-tips-and-advice.jpg?fit=624%2C351&amp;ssl=1" data-orig-size="624,351" data-comments-opened="1" data-image-meta="{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;1&quot;}" data-image-title="claimed and shamed features CEGA fraud tips and advice" data-image-description="" data-medium-file="https://i1.wp.com/insurance-edge.net/wp-content/uploads/2019/10/claimed-and-shamed-features-CEGA-fraud-tips-and-advice.jpg?fit=300%2C169&amp;ssl=1" data-large-file="https://i1.wp.com/insurance-edge.net/wp-content/uploads/2019/10/claimed-and-shamed-features-CEGA-fraud-tips-and-advice.jpg?fit=620%2C349&amp;ssl=1" data-recalc-dims="1" />
<h2>How to prevent opportunistic insurance fraud</h2>
Working with the Insurance Fraud Bureau (IFB), we conducted research to investigate ways of preventing opportunistic insurance fraud. In a carefully designed experiment, we replicated online motor insurance claims and applications processes. With over 12,000 participants, we tested ways of encouraging honesty when completing the forms by inserting behavioural science-inspired messages (behavioural interventions) in front of questions that customers may lie or exaggerate in response to, in order to get a better deal for themselves. The results were impressive, with the behavioural interventions reducing dishonesty by an average of 36% 4.
Replicating these findings in the real world could save the insurance industry hundreds of millions of pounds each year in the UK alone.
In summary, opportunistic insurance fraud is a big problem for the industry and an extremely costly one. Existing means of detecting such fraud, and the increasingly sophisticated ways of doing so are important. However, behavioural science offers an additional approach in the form of using behavioural interventions to prevent opportunistic fraud from happening in the first place. This approach may prove more effective, as well as much simpler, easier, less risky and less costly. As such, prevention is a process insurers need to take seriously.
In order to do so they should first undertake an audit of their existing operational communication platforms to identify where interventions could be applied. They should then field trial these to confirm their effectiveness, before rolling out a full-scale implementation across all platforms.
These steps can be taken alongside traditional detection processes, with the two complementing each other. In short, insurers that leverage the best of both worlds – prevention and detection – are likely to see better outcomes both for their own bottom lines and those of their honest customers.
<ul>
<li><a href="https://dectech.co.uk/news-insights/buzz/why-the-insurance-industry-needs-to-tackle-opportunistic-fraud/" target="_blank" rel="noopener noreferrer">https://dectech.co.uk/news-insights/buzz/why-the-insurance-industry-needs-to-tackle-opportunistic-fraud/</a></li>
<li><a href="https://www.mckinsey.com/~/media/McKinsey/Industries/Financial%20Services/Our%20Insights/Claims%20management%20Taking%20a%20determined%20stand%20against%20insurance%20fraud/Claims_management_taking_a_determined_stand_against_fraud.pdf" target="_blank" rel="noopener noreferrer">https://www.mckinsey.com/~/media/McKinsey/Industries/Financial%20Services/Our%20Insights/Claims%20management%20Taking%20a%20determined%20stand%20against%20insurance%20fraud/Claims_management_taking_a_determined_stand_against_fraud.pdf</a></li>
<li><a href="https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/494105/PU1817_Insurance_Fraud_Taskforce.pdf" target="_blank" rel="noopener noreferrer">https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/494105/PU1817_Insurance_Fraud_Taskforce.pdf</a></li>
<li><a href="https://www.ingentaconnect.com/content/hsp/ama/2020/00000005/00000004/art00002;jsessionid=3frs0j9nuhts9.x-ic-live-01" target="_blank" rel="noopener noreferrer">https://www.ingentaconnect.com/content/hsp/ama/2020/00000005/00000004/art00002;jsessionid=3frs0j9nuhts9.x-ic-live-01</a></li>
</ul>

Insurance Edge is always interested in new ideas, especially when those techniques have been studied and assessed in a real world scenario, via the Insurance Fraud Bureau. In this article Dr Tim Mitchell and Dr Benny Cheung take a look at fraud and how patterns of human behaviour can be modified. Prevention can sometimes be […] More

Can Behavioural Science Help to Prevent Insurance Fraud?

If you’ve seen your weekly screen time go up over the past few months, you’re hardly alone. Maggie Stanphill, Google’s director of user experience (UX), has seen her stats go up, too. Maggie leads UX efforts for Google’s Digital Wellbeing initiative, and she’s noticed that the current state of the world requires an evolution in our understanding of how we can mindfully use tech. 
“The emphasis on screen time feels really tone deaf right now, right?” Maggie says. “Almost everyone who has access to tech is spending more and more time online. So many of us are getting those weekly screen time reports that say it’s gone up by some percent, and that might actually be aggravating.” But, she explains, there are ways digital tools can be more helpful. “We’re really trying to create a more nuanced approach. Look at sleep: We know queries for insomnia have gone way up, and we’re working on refining our tools to support that.”
I recently sat down with Maggie via Google Meet to talk more about her work in UX, and how Google’s Digital Wellbeing features are pivoting to meet people where they are. 
How would you describe your job at a dinner party to someone who doesn’t work in tech?
I’d say we conduct research with people around the world to better understand their needs and bring that perspective into the product design process to make tech more helpful and less intrusive. I also like to say that you can think of a UXer as the voice of the user in the room where decisions are being made. That’s where we play a role to advocate for people’s needs. 
What was your career path to UX like? 
I started as a journalist, actually; I got a degree in English. I loved storytelling, and I really found there was a natural transition when I moved into this field—storytelling was part of the design process. Narrative showed up in a variety of ways, like conducting ethnographic research and sharing people’s perspectives through tools like personas, which are character sketches that help UXers understand their core audience. Having that people-first lens is what’s really driven my career path. There was no “UX degree” when I was going through school, but I’ve found that focusing on understanding people’s needs and their goals translated really well in terms of what I needed to grow and be effective at this work. 
Have you heard of the Strengths Finder quiz?
I’ve heard of it but I haven’t taken it!
One of my primary strengths is “input,” information gathering and synthesizing. I’m organizing information every day, in my brain and in Google Docs! It’s part of my process. I have to internalize things to feel like I can be fluent and translate those concepts to make sure our products are building toward a shared strategy and are easy to use. 
What specifically are you working on at Google right now?
My focus is two-fold. I work in an advocate role for the company-wide digital wellbeing initiative, and I also manage our Fit UX team. My interest in people and human behavior is very much what drew me to digital wellbeing. For the past two years we’ve spent our time defining what “digital wellbeing” means. More recently, we’ve tried to pivot from a focus on screen time as the most important metric to really empowering people by default. What I mean by that is there are ways we as product designers can build wellbeing into our product experiences, so people don’t have so much to worry about when it comes to using tech. Because we’re a cross-Google team, we’re really focused on providing expertise from a research-based set of best practices. So we established a digital wellbeing toolkit, which includes four key tenets: empowerment, awareness, control and adaptability, and applied those in a variety of ways. 
How do those tenets show up in products?
Sleep is a great example of how people’s fundamental needs drive Google’s product priorities. Sleep is so critical to overall wellbeing, and we’ve heard from people all over the world that they’re not getting enough of it. We imagined we could help people get more sleep by making our products more adaptable. You look at Android, Google Assistant and YouTube and realize that if they were more coordinated they could work together to help people get more sleep. That’s when we came up with Bedtime mode, which uses Clock to set your preferred schedule. That schedule then activates features like Grayscale and Do Not Disturb to help you disconnect, and stay that way. 
The underlying tenet that makes this work is adaptability, where each experience takes the person’s preferences into account; in this case, that preference is their bedtime schedules. Then all the devices and apps adjust to support those needs: Your bedtime clock notifies you to wind down, apps turn to Grayscale, and when your phone docks, it goes into Do Not Disturb automatically. Google Assistant also has a Bedtime routine that follows this schedule.

If you’ve seen your weekly screen time go up over the past few months, you’re hardly alone. Maggie Stanphill, Google’s director of user experience (UX), has seen her stats go up, too. Maggie leads UX efforts for Google’s Digital Wellbeing initiative, and she’s noticed that the current state of the world requires an evolution in […] More

Maggie Stanphill is making more mindful tech

Welcome to the latest installment of our blog series “My Path to Google.” These are real stories from Googlers, interns and alumni highlighting how they got to Google, what their roles are like and even some tips on how to prepare for interviews.
Today’s post is all about Awa Dieng, an AI Resident on the Google Brain team in our Ghana office. Awa shares her path to working in research and machine learning at Google and how her work ensures AI systems are beneficial for everyone. If you’re interested in learning more, applications for the Google AI Residency will open in early 2021.
What first sparked your interest in working in research?
I was born and raised in Kaolack, Senegal, a country in West Africa. In school, I was always drawn to science in general and mathematics in particular. After high school, I received a government scholarship to study in France, where I received a broad education in math, physics and computer science.
As a student specializing in applied math, I started to get interested in the field of artificial intelligence (AI). I was excited about the possibilities surrounding emerging AI and machine learning (ML), and given my background and interests, research in ML seemed like a great fit. 
So I pursued my first research experience—interning with the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) team at Cornell University. I also worked on ML research in an academic setting at Duke University, but I was looking to diversify my experience by working in an industry research lab, which led me to apply to work at Google.
Of course, I was aware of the Google Brain team, which is highly respected in the community and publishes important work at all major ML conferences. The AI residency seemed like the perfect opportunity to learn from these researchers and explore different areas of machine learning. 
How would you describe your role at Google?
I work on the Brain team as an AI resident. The Google AI Residency is a year-long program designed to train and support the next generation of deep learning researchers. My time is spent identifying interesting problems in machine learning and working with my collaborators to solve them. This includes reading the existing literature on ML, running experiments and writing papers. 
Specifically, my research is centered around machine learning and causality, which aims at identifying cause and effect and answering “what if” questions. Indeed, while machine learning has led to a lot of progress in recent years, its widespread use has highlighted issues regarding bias, reliability and transparency. These are particularly important when ML systems are used to make consequential decisions that impact people’s lives. I believe a causal perspective can address these failures, and my work aims to draw strength from these two fields to build better decision-making systems.

Welcome to the latest installment of our blog series “My Path to Google.” These are real stories from Googlers, interns and alumni highlighting how they got to Google, what their roles are like and even some tips on how to prepare for interviews. Today’s post is all about Awa Dieng, an AI Resident on the […] More

How Awa Dieng found her passion for machine learning

Imagine whistling your favorite song. You might not sound like the real deal, right? Now imagine your rendition using auto-tune software. Better, sure, but the result is still your voice. What if there was a way to turn your voice into something like a violin, or a saxophone, or a flute? 
Google Research’s Magenta team, which has been focused on the intersection of machine learning and creative tools for musicians, has been experimenting with exactly this. The team recently created an open source technology called Differentiable Digital Signal Processing (DDSP). DDSP is a new approach to machine learning that enables models to learn the characteristics of a musical instrument and map them to a different sound. The process can lead to so many creative, quirky results. Try replacing a capella singing with a saxophone solo, or a dog barking with a trumpet performance. The options are endless. 
And so are the sounds you can make. This development is important because it enables music technologies to become more inclusive. Machine learning models inherit biases from the datasets they are trained on, and music models are no different. Many are trained on the structure of western musical scores, which excludes much of the music from the rest of the world. Rather than following the formal rules of western music, like the 12 notes on a piano, DDSP transforms sound by modeling frequencies in the audio itself. This opens up machine learning technologies to a wider range of musical cultures. 
In fact, anyone can give it a try.  We created a tool called Tone Transfer to allow musicians and amateurs alike to tap into DDSP as a delightful creative tool. Play with the Tone Transfer showcase to sample sounds, or record your own, and listen to how they can be transformed into a myriad of instruments using DDSP technology. Check out our film that shows artists using Tone Transfer for the first time.
DDSP does not create music on its own; think of it like another instrument that requires skill and thought. It’s an experimental soundscape environment for music, and we’re so excited to see how the world uses it.

Imagine whistling your favorite song. You might not sound like the real deal, right? Now imagine your rendition using auto-tune software. Better, sure, but the result is still your voice. What if there was a way to turn your voice into something like a violin, or a saxophone, or a flute?  Google Research’s Magenta team, […] More

What if you could turn your voice into any instrument?

Why is this field of work so important?
Machine learning is such a powerful tool, and because of that, you want to make sure you’re using it responsibly. Let’s take an electric machine saw as an example. It’s a super powerful tool, but you need to learn how to use it in order not to cut your fingers. Once you learn, it’s so useful and efficient that you’ll never want to go back to using a hand saw. And the same goes for machine learning. We want to help you understand and use machine learning correctly, fairly and safely.
Since machine learning is used in our everyday lives, it’s also important for everyone to understand how it impacts us. No matter whether you’re a coffee shop owner using machine learning to optimize the purchase of your beans based on seasonal trends, or your doctor diagnoses you with a disease with the help of this technology, it’s often crucial to understand why a machine learning model has produced the outcome it has. It’s also important for developers and decision-makers to be able to explain or present a machine learning model to people in order to do so. This is what we call “interpretability.”
How do you make machine learning models easier to understand and interpret? 
There are many different ways to make an ML model easier to understand. One way is to make the model reflect how humans think from the start, and have the model &#8220;trained&#8221; to provide explanations along with predictions, meaning when it gives you an outcome, it also has to explain how it got there.
Another way is to try and explain a model after the training on data is done. This is something you can do when the model has been built to use input to provide an output from its own perspective, optimizing for prediction, without a clear “how” included. This means you&#8217;re able to plug things into it and see what comes out, and that can give you some insight into how the model generally makes decisions, but you don&#8217;t necessarily know exactly how specific inputs are interpreted by the model in specific cases.
One way to try and explain models after they’ve been trained is using low level features or high level concepts. Let me give you an example of what this means. Imagine a system that classifies pictures: you give it a picture and it says, “This is a cat.” A low level feature is when I then ask the machine which pixels mattered for that prediction, it can tell us if it was one pixel or the other, and we might be able to see that the pixels in question show the cat’s whiskers. But we might also see that it is a scattering of pixels that don’t appear meaningful to the human eye, or that it’s made the wrong interpretation. High level concepts are more similar to the way humans communicate with one another. Instead of asking about pixels, I’d ask, “Did the whiskers matter for the prediction? or the paws?” and again, the machine can show me what imagery led it to reach this conclusion. Based on the outcome, I can understand the model better. (Together with researchers from Stanford, we’ve published papers that go into further detail on this for those who are interested.)
Can machines understand some things that we humans can’t? 
Yes! This is an area that I am very interested in myself. I am currently working on a way to showcase how technology can help humans learn new things. Machine learning technology is better at some things than we are; for example it can analyze and interpret data at a much larger scale than humans can. Leveraging this technology, I believe we can enlighten human scientists with knowledge they haven&#8217;t previously been aware of.
What do you need to be careful of when you’re making conclusions based on machine learning models?
First of all, we have to be careful that human bias doesn&#8217;t come into play. Humans carry biases that we simply cannot help and are often unaware of, so if an explanation is up to a human’s interpretation, and often it is, then we have a problem. Humans read what they want to read. Now, this doesn’t mean that you should remove humans from the loop. Humans communicate with machines, and vice versa. Machines need to communicate their outcomes in the form of a clear statement using quantitative data, not one that is vague and completely open for interpretation. If the latter happens, then the machine hasn’t done a very good job and the human isn’t able to provide good feedback to the machine. It could also be that the outcome simply lacks additional context only the human can provide, or that it could benefit from having caveats, in order for them to make an informed judgement about the results of the model.
What are some of the main challenges of this work? 
Well, one of the challenges for computer scientists in this field is dealing with non mathematical objectives, which are things you might want to optimize for, but don’t have an equation for. You can’t always define what is good for humans using math. That requires us to test and evaluate methods with rigor, and have a table full of different people to discuss the outcome. Another thing has to do with complexity. Humans are so complex that we have a whole field of work &#8211; psychology &#8211; to study this. So in my work, we don&#8217;t just have computational challenges, but also complex humans that we have to consider. Value-based questions such as “what defines fairness?” are even harder. They require interdisciplinary collaboration, and a diverse group of people in the room to discuss each individual matter.
What&#8217;s the most exciting part? 
I think interpretability research and methods are making a huge impact. Machine learning technology is a powerful tool that will transform society as we know it, and helping others to use it safely is very rewarding.
On a more personal note, I come from South Korea and grew up in circumstances where I feel I didn’t have too many opportunities. I was incredibly lucky to get a scholarship to MIT and come to the U.S. When I think about the people who haven&#8217;t had these opportunities to be educated in science or machine learning, and knowing that this machine learning technology can really help and be useful to them in their everyday lives if they use it safely, I feel really motivated to be working on democratizing this technology. There&#8217;s many ways to do it, and interpretability is one of the things that I can contribute with.


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Why is this field of work so important? Machine learning is such a powerful tool, and because of that, you want to make sure you’re using it responsibly. Let’s take an electric machine saw as an example. It’s a super powerful tool, but you need to learn how to use it in order not to […] More

How do machine learning models explain themselves?

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Startit, based in Belgrade, Serbia, is on a mission to give new life to the Serbian economy. Serbia, like many countries in Southeast Europe, has faced economic stagnation for decades, with much of its best local tech talent moving abroad in search of better opportunities. To battle this issue, Startit developed a plan: work together with Launchpad to create an entrepreneurship curriculum and raise up a new generation of startup founders. “We have world-class engineers in Serbia, but if we taught them how to make a business around the amazing technology, it would have a meaningful impact on their careers, their families and our entire economy,” says Vukasin Stojkov, Startit’s founder. Startit created a semester-long course for more than 20 students at Belgrade’s Computer Science College (RAF). During the program, these students learned about key startup concepts and met with some of the most successful entrepreneurs in Serbia. Computer science student Milica Kostic came to the course with a few startup ideas. By the end, she was already taking action with two of her friends from the group. “We have an idea to help deaf people by making it easier for them to communicate with people who are not familiar with sign language,” Milica says. “The course showed us that it is possible to make it, and I really believe that I can do the same.”<h3>Rethinking how Indonesians eat with Wahyoo and Digitaraya </h3>


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Startit, based in Belgrade, Serbia, is on a mission to give new life to the Serbian economy. Serbia, like many countries in Southeast Europe, has faced economic stagnation for decades, with much of its best local tech talent moving abroad in search of better opportunities.  To battle this issue, Startit developed a plan: work together […] More

How Launchpad helps startups solve global problems

During their early studies and careers, Tiffany Deng, Tulsee Doshi and Timnit Gebru found themselves asking the same questions: Why is it that some products and services work better for some than others, and why isn’t everyone represented around the table when a decision is being made? Their collective passion to create a digital world that works for everyone is what brought the three women to Google, where they lead efforts to make machine learning systems fair and inclusive. I sat down with Tiffany, Tulsee and Timnit to discuss why working on machine learning fairness is so important, and how they came to work in this field.   How would you explain your job to someone who isn&#8217;t in tech?Tiffany: I’d say my job is to make sure we’re not reinforcing any of the entrenched and embedded biases humans might have into products people use, and that every time you pick up a product—a Google product—you as an individual can have a good experience when using it. Timnit: I help machines understand imagery and text. Just like a human, if a machine tries to learn a pattern or understand something, and it is trained on input that’s been provided for it to do just that, the input, or data in this case, has societal bias. This could lead to a biased outcome or prediction made by the machine. And my work is to figure out different ways of mitigating this bias. Tulsee: My work includes making sure everyone has positive experiences with our products, and that people don’t feel excluded or stereotyped, especially based on their identities. The products should work for you as an individual, and provide the best experience possible. What made you want to work in this field?Tulsee: When I started college, I was unsure of what I wanted to study. I came in with an interest in math, and quickly found myself taking a variety of classes in computer science, among other topics. But no matter which interesting courses I took, I often felt a disconnect between what I was studying and the people the work would help. I kept coming back to wanting to focus on people, and after taking classes like child psychology and philosophy of AI, I decided I wanted to take on a role where I could combine my skill sets with a people-centered approach. I think everyone has an experience of services and technology not working for them, and solving for that is a passion behind much of what I do. Tiffany: After graduating from West Point I joined the army as an intelligence officer before becoming a consultant and working for the State Department and the Department of Defense. I then joined Facebook as a privacy manager for a period of time, and that’s when I started working on more ML fairness-related matters. When people ask me how I ended up where I am, I’d say that there’s never a straight path to finding your passion, and all the experiences that I’ve had outside of tech are ones I bring into the work I’m doing today. An important “aha moment” for me was about a year and a half ago, when my son had a rash all over his body and we went to the doctor to get help. They told us they weren’t able to diagnose him because his skin wasn’t red, and of course, his skin won’t turn red as he has deep brown skin. Someone telling me they can’t diagnose my son because of his skin—that’s troubling as a parent. I wanted to understand the root cause of the issue—why is this not working for me and my family, the way it does for others? Fast forwarding, when thinking about how AI will someday be ubiquitous and an important component in assisting human decision-making, I wanted to get involved and help ensure that we’re building technology that works equally as well for everyone. Timnit: I grew up with a father and two sisters working in electrical engineering, so I followed their path and decided to also pursue studies in the field. After spending some time at Apple working as a circuit designer and starting my own company, I went back to studying image processing and completed a Ph.D. in computer vision. Towards the end of my Ph.D., I read a ProPublica article discussing racial bias in predicting crime recidivism rates. At the same time, I started thinking more about how there were very few, if any, Black people in grad school and that whenever I went to conferences, Black people weren’t represented in the decisions driving this field of work. That’s how I came to found a nonprofit organization called Black in AI, along with Rediet Abebe, to increase the visibility of Black people working in the field. After graduating with my Ph.D. I did a postdoc at Microsoft research and soon after that, I took a role at Google as the co-lead of the ethical AI research team which was founded by Meg Mitchell. What are some of the main challenges in this work, and why is it so important? Tulsee: The challenge question is interesting, and a hard one. First of all, there is the theoretical and sociological question on the notion of fairness—how does one define what is fair? Addressing fairness concerns requires multiple perspectives, and product development approaches ranging from technical to design. Because of this, even for use cases where we have a lot of experience, there are still many challenges for product teams to understand the different approaches for measuring and tackling fairness concerns. This is one of the reasons why I believe tooling and resources are so critical, and why we’re investing in them for both internal and external purposes.Another important aspect is company culture and how companies define their values and motivate their employees. We are starting to see a growing, industry-wide shift in terms of what success looks like. If organizations and product creators get rewarded for thinking about a broader set of people when developing products, the more companies start fostering a diverse workforce, consult external experts and think about whose voices are being represented at the table. We need to remember we’re talking about real people&#8217;s experiences, and while working on these issues can sometimes be emotionally difficult, it’s so important to get right. Timnit: A general challenge is that people who are the most negatively affected are often the ones whose voices are not heard. Representation is an important issue, and while there’s a lot of opportunities with ML technology in society, it’s important to have a diverse set of people and perspectives involved when working on the development so you don’t end up enhancing a gap between different groups.This is not an issue that is specific to ML. As an example, let’s think of DNA sequencing. The African continent has the most diverse DNA in the world, but I read that it consists of less than 1 percent of the DNA studied in DNA sequencing, so there are examples of researchers who have come to the wrong conclusions based on data that was not representative. Now imagine someone is looking to develop the next generation of drugs, and the result could be that they don’t work for certain groups because their DNA hasn’t been rightly represented. Do you think ML has the potential to help complement human decision making, and drive the world to become more fair?Timnit: It’s important to recognize the complexity of the human mind, and that humans should not be replaced when it comes to decision making. I don’t think ML can make the world more fair: Only humans can do that. And humans choose how to use this technology. In terms of opportunities, there are many ways in which we have already used ML systems to uncover societal bias, and this is something I work on as well. For example, studies by Jennifer Eberhardt and her collaborators at Stanford University including Vinodkumar Prabhakaran, who has since joined our team, used natural language processing to analyze body camera recordings of police stops in Oakland. They found a pattern of police speaking less respectfully to Black people than white people. A lot of times when you show these issues backed up by data and scientific analysis, it can help make a case. At the same time, the history of scientific racism also shows that data can be used to propagate the most harmful societal biases of the day. Blindly trusting data driven studies or decisions can be dangerous. It’s important to understand the context under which these studies are conducted and to work with affected communities and other domain experts to formulate the questions that need to be addressed.Tiffany: I think ML will be incredibly important to help with things like climate change, sustainability and helping save endangered animals. Timnit’s work on using AI to help identify diseased cassava plants is an incredible use of AI, especially in the developing world. The range of problems AI can aid humans with is endless—we just have to ensure we continue to build technological solutions with ethics and inclusion at the forefront of our conversations. 

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During their early studies and careers, Tiffany Deng, Tulsee Doshi and Timnit Gebru found themselves asking the same questions: Why is it that some products and services work better for some than others, and why isn’t everyone represented around the table when a decision is being made? Their collective passion to create a digital world […] More

Meet the Googlers working to ensure tech is for everyone

When Fernanda Viégas was in college, it took three years with three different majors before she decided she wanted to study graphic design and art history. And even then, she couldn’t have imagined the job she has today: building artificial intelligence and machine learning with fairness and transparency in mind to help people in their daily lives.  Today Fernanda, who grew up in Rio de Janeiro, Brazil, is a senior researcher at Google. She’s based in London, where she co-leads the global People + AI Research (PAIR) Initiative, which she co-founded with fellow senior research scientist Martin M. Wattenberg and Senior UX Researcher Jess Holbrook, and the Big Picture team. She and her colleagues make sure people at Google think about fairness and values–and putting Google’s AI Principles into practice–when they work on artificial intelligence. Her team recently launched a series of “AI Explorables,&#8221; a collection of interactive articles to better explain machine learning to everyone. When she’s not looking into the big questions around emerging technology, she’s also an artist, known for her artistic collaborations with Wattenberg. Their data visualization art is a part of the permanent collection of the Museum of Modern Art in New York.  I recently sat down with Fernanda via Google Meet to talk about her role and the importance of putting people first when it comes to AI. 


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When Fernanda Viégas was in college, it took three years with three different majors before she decided she wanted to study graphic design and art history. And even then, she couldn’t have imagined the job she has today: building artificial intelligence and machine learning with fairness and transparency in mind to help people in their […] More

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