FRA’s report is titled Getting The Future Right and opens with some of the ways AI is already making lives better—such as helping with cancer diagnosis, and even predicting where burglaries are likely to take place.

“The possibilities seem endless,” writes Michael O’Flaherty, Director of the FRA, in the report’s foreword. “But how can we fully uphold fundamental rights standards when using AI?”

The FRA interviewed over a hundred public administration officials, private company staff, and a diverse range of experts, in a bid to answer that question.

With evidence of algorithms having biases which could lead to automating societal issues like racial profiling—it’s a question that needs answering if the full potential of AI is going to be unlocked for the whole of society.

O’Flaherty says:

“AI is not infallible, it is made by people – and humans can make mistakes. That is why people need to be aware when AI is used, how it works and how to challenge automated decisions. The EU needs to clarify how existing rules apply to AI. And organisations need to assess how their technologies can interfere with people’s rights both in the development and use of AI.

“We have an opportunity to shape AI that not only respects our human and fundamental rights but that also protects and promotes them.”

AI is being used in almost every industry in some form or another—if not already, it will be soon.

Biases in AI are more dangerous in some industries than others. Policing is an obvious example, but in areas like financial services it could mean one person being given a loan or mortgage compared to another.

Without due transparency, these biases could happen without anyone knowing the reasons behind such decisions—it could simply be because someone grew up in a different neighbourhood. Each automated decision has a very real human impact.

The FRA calls for the EU to:

• Make sure that AI respects ALL fundamental rights – AI can affect many rights – not just privacy or data protection. It can also discriminate or impede justice. Any future AI legislation has to consider this and create effective safeguards.
• Guarantee that people can challenge decisions taken by AI – people need to know when AI is used and how it is used, as well as how and where to complain. Organisations using AI need to be able to explain how their systems take decisions.
• Assess AI before and during its use to reduce negative impacts – private and public organisations should carry out assessments of how AI could harm fundamental rights.
• Provide more guidance on data protection rules – the EU should further clarify how data protection rules apply to AI. More clarity is also needed on the implications of automated decision-making and the right to human review when AI is used.
• Assess whether AI discriminates – awareness about the potential for AI to discriminate, and the impact of this, is relatively low. This calls for more research funding to look into the potentially discriminatory effects of AI so Europe can guard against it.
• Create an effective oversight system – the EU should invest in a more ‘joined-up’ system to hold businesses and public administrations accountable when using AI. Authorities need to ensure that oversight bodies have adequate resources and skills to do the job.

The EU has increased its scrutiny of “big tech” companies like Google in recent years over concerns of invasive privacy practices and abusing their market positions. Last week, AI News reported that Google had controversially fired leading AI ethics researcher Timnit Gebru after she criticised her employer in an email.

Google chief executive Sundar Pichai wrote in a memo: “We need to accept responsibility for the fact that a prominent black, female leader with immense talent left Google unhappily.

“It’s incredibly important to me that our black, women, and under-represented Googlers know that we value you and you do belong at Google.”

Gebru gave an interview to the BBC this week in which she called Google and big tech “institutionally racist”. With that in mind, the calls made in the FRA’s report seem especially important to heed.

You can download a full copy of the FRA’s report here.

(Photo by Guillaume Périgois on Unsplash)

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A browse through our ‘ethics’ category here on AI News will highlight the serious problem of bias in today’s algorithms. With AIs being increasingly used for decision-making, parts of society could be left behind.

Conducted by the Centre for Data Ethics and Innovation (CDEI), the investigation will focus on areas where AI has tremendous potential – such as policing, recruitment, and financial services – but would have a serious negative impact on lives if not implemented correctly.

Digital Secretary Jeremy Wright said:

“Technology is a force for good which has improved people’s lives but we must make sure it is developed in a safe and secure way.

Our Centre for Data Ethics and Innovation has been set up to help us achieve this aim and keep Britain at the forefront of technological development.

I’m pleased its team of experts is undertaking an investigation into the potential for bias in algorithmic decision-making in areas including crime, justice and financial services. I look forward to seeing the Centre’s recommendations to Government on any action we need to take to help make sure we maximise the benefits of these powerful technologies for society.”

Durham police are currently using AI for a tool it calls ‘Harm Assessment Risk’. As you might guess, the AI determines whether an individual is likely to cause further harm. The tool helps with decisions on whether an individual is eligible for deferred prosecution.

If an algorithm is more or less effective on individuals with different characteristics over another, serious problems would arise.

Roger Taylor, Chair of the CDEI, is expected to say during a Downing Street event:

“The Centre is focused on addressing the greatest challenges and opportunities posed by data driven technology. These are complex issues and we will need to take advantage of the expertise that exists across the UK and beyond. If we get this right, the UK can be the global leader in responsible innovation.

We want to work with organisations so they can maximise the benefits of data driven technology and use it to ensure the decisions they make are fair. As a first step we will be exploring the potential for bias in key sectors where the decisions made by algorithms can have a big impact on people’s lives.

I am delighted that the Centre is today publishing its strategy setting out our priorities.”

In a 2010 study, researchers at NIST and the University of Texas in Dallas found (PDF) algorithms designed and tested in East Asia are better at recognising East Asians, while those developed in Western countries are more accurate when detecting Caucasians.

Similar worrying discrepancies were highlighted by Algorithmic Justice League founder Joy Buolamwini during a presentation at the World Economic Forum back in January. For her research, she analysed popular facial recognition algorithms.

These issues with bias in algorithms need to be addressed now before they are used for critical decision-making. The public is currently unconvinced AI will benefit humanity, and AI companies themselves are bracing for ‘reputational harm’ along the way.

Interim reports from the CDEI will be released in the summer with final reports set to be published early next year.

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Buolamwini is also an MIT Media Lab researcher and went somewhat viral for her TED Talk in 2016 titled ‘How I’m fighting bias in algorithms’.

Her latest speech included a presentation in which Buolamwini went over an analysis of the current popular facial recognition algorithms.

Here were the overall accuracy results when guessing the gender of a face:

• Microsoft: 93.7 percent
• Face++: 90 percent
• IBM: 87.9 percent

Shown in this way, there appears to be little problem. Of course, society is a lot more diverse and algorithms need to be accurate for all.

When separated between males and females, a greater disparity becomes apparent:

• Microsoft: 89.3 percent (females), 97.4 percent (males)
• Face++: 78.7 percent (females), 99.3 percent (males)
• IBM: 79.7 percent (females), 94.4 percent (males)

Here we begin to see the underrepresentation of females in STEM careers begin to come into effect. China-based Face++ suffers the worst, likely a result of the country’s more severe gender gap (PDF) over the US.

Splitting between skin type also increases the disparity:

• Microsoft: 87.1 percent (darker), 99.3 percent (lighter)
• Face++: 83.5 percent (darker), 95.3 percent (lighter)
• IBM: 77.6 percent (darker), 96.8 percent (lighter)

The difference here is likely again to do with a racial disparity in STEM careers. A gap between 12-19 percent is observed between darker and lighter skin tones.

So far, the results are in line with a 2010 study by researchers at NIST and the University of Texas in Dallas. The researchers found (PDF) algorithms designed and tested in East Asia are better at recognising East Asians, while those developed in Western countries are more accurate when detecting Caucasians.

“We did something that hadn’t been done in the field before, which was doing intersectional analysis,” explains Buolamwini. “If we only do single axis analysis – we only look at skin type, only look at gender… – we’re going to miss important trends.”

Here is where the results get most concerning. Results are in descending order from most accurate to least:

Microsoft

Lighter Males (100 percent)

Lighter Females (98.3 percent)

Darker Males (94 percent)

Darker Females (79.2 percent)

Face++

Darker Males (99.3 percent)

Lighter Males (99.2 percent)

Lighter Females (94 percent)

Darker Females (65.5 percent)

IBM

Lighter Males (99.7 percent)

Lighter Females (92.9 percent)

Darker Males (88 percent)

Darker Females (65.3 percent)

The lack of accuracy with regards to females with darker skin tones is of particular note. Two of the three algorithms would get it wrong in approximately one-third of occasions.

Just imagine surveillance being used with these algorithms. Lighter skinned males would be recognised in most cases, but darker skinned females would be stopped often. That could be a lot of mistakes in areas with high footfall such as airports.

Prior to making her results public, Buolamwini sent the results to each company. IBM responded the same day and said their developers would address the issue.

When she reassessed IBM’s algorithm, the accuracy when assessing darker males jumped from 88 percent to 99.4 percent, for darker females from 65.3 percent to 83.5 percent, for lighter females from 92.9 percent to 97.6 percent, and for lighter males it stayed the same at 97 percent.

Buolamwini commented: “So for everybody who watched my TED Talk and said: ‘Isn’t the reason you weren’t detected because of, you know, physics? Your skin reflectance, contrast, et cetera,’ — the laws of physics did not change between December 2017, when I did the study, and 2018, when they launched the new results.”

“What did change is they made it a priority.”

You can watch Buolamwini’s full presentation at the WEF here.

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