Entrepreneurs Taking on Bias in Artificial Intelligence

Whether it’s a navigation app such as Waze, a music recommendation service such as Pandora or a digital assistant such as Siri, odds are you’ve used artificial intelligence in your everyday life. “Today 85 percent of Americans use AI every day,” says Tess Posner, CEO of AI4ALL. AI has also been touted as the new must-have […]

Whether it’s a navigation app such as Waze, a music recommendation service such as Pandora or a digital assistant such as Siri, odds are you’ve used artificial intelligence in your everyday life.

“Today 85 percent of Americans use AI every day,” says Tess Posner, CEO of AI4ALL.

AI has also been touted as the new must-have for business, for everything from customer service to marketing to IT. However, for all its usefulness, AI also has a dark side. In many cases, the algorithms are biased.

Some of the examples of bias are blatant, such as Google’s facial recognition tool tagging black faces as gorillas or an algorithm used by law enforcement to predict recidivism disproportionately flagging people of color. Others are more subtle. When Beauty.AI held an online contest judged by an algorithm, the vast majority of “winners” were light-skinned. Search Google for images of “unprofessional hair” and the results you see will mostly be pictures of black women (even searching for “man” or “woman” brings back images of mostly white individuals).

While more light has been shined on the problem recently, some feel it’s not an issue addressed enough in the broader tech community, let alone in research at universities or the government and law enforcement agencies that implement AI.

“Fundamentally, bias, if not addressed, becomes the Achilles’ heel that eventually kills artificial intelligence,” says Chad Steelberg, CEO of Veritone. “You can’t have machines where their perception and recommendation of the world is skewed in a way that makes its decision process a non-sequitur from action. From just a basic economic perspective and a belief that you want AI to be a powerful component to the future, you have to solve this problem.”

As artificial intelligence becomes ever more pervasive in our everyday lives, there is now a small but growing community of entrepreneurs, data scientists and researchers working to tackle the issue of bias in AI. I spoke to a few of them to learn more about the ongoing challenges and possible solutions.

Cathy O’Neil, founder of O’Neil Risk Consulting & Algorithmic Auditing

Solution: Algorithm auditing

Back in the early 2010s, Cathy O’Neil was working as a data scientist in advertising technology, building algorithms that determined what ads users saw as they surfed the web. The inputs for the algorithms included innocuous-seeming information like what search terms someone used or what kind of computer they owned.

Cathy O’Neil, founder of O’Neil Risk Consulting & Algorithmic Auditing

However, O’Neil came to realize that she was actually creating demographic profiles of users. Although gender and race were not explicit inputs, O’Neil’s algorithms were discriminating against users of certain backgrounds, based on the other cues.

As O’Neil began talking to colleagues in other industries, she found this to be fairly standard practice. These biased algorithms weren’t just deciding what ads a user saw, but arguably more consequential decisions, such as who got hired or whether someone would be approved for a credit card. (These observations have since been studied and confirmed by O’Neil and others.)

What’s more, in some industries — for example, housing — if a human were to make decisions based on the specific set of criteria, it likely would be illegal due to anti-discrimination laws. But, because an algorithm was deciding, and gender and race were not explicitly the factors, it was assumed the decision was impartial.

“I had left the finance [world] because I wanted to do better than take advantage of a system just because I could,” O’Neil says. “I’d entered data science thinking that it was less like that. I realized it was just taking advantage in a similar way to the way finance had been doing it. Yet, people were still thinking that everything was great back in 2012. That they were making the world a better place.”

O’Neil walked away from her adtech job. She wrote a book, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracyabout the perils of letting algorithms run the world, and started consulting.

Eventually, she settled on a niche: auditing algorithms.

“I have to admit that it wasn’t until maybe 2014 or 2015 that I realized this is also a business opportunity,” O’Neil says.

Right before the election in 2016, that realization led her to found O’Neil Risk Consulting & Algorithmic Auditing (ORCAA).

“I started it because I realized that even if people wanted to stop that unfair or discriminatory practices then they wouldn’t actually know how to do it,” O’Neil says. “I didn’t actually know. I didn’t have good advice to give them.” But, she wanted to figure it out.

So, what does it mean to audit an algorithm?

UK Report Urges Action to Combat AI Bias, Ensure Diversity in Data Sets

The need for diverse development teams and truly representational data-sets to avoid biases being baked into AI algorithms is one of the core recommendations in a lengthy Lords committee report looking into the economic, ethical and social implications of artificial intelligence, and published today by the upper House of the UK parliament. “The main ways to address […]

The need for diverse development teams and truly representational data-sets to avoid biases being baked into AI algorithms is one of the core recommendations in a lengthy Lords committee report looking into the economic, ethical and social implications of artificial intelligence, and published today by the upper House of the UK parliament.

“The main ways to address these kinds of biases are to ensure that developers are drawn from diverse gender, ethnic and socio-economic backgrounds, and are aware of, and adhere to, ethical codes of conduct,” the committee writes, chiming with plenty of extant commentary around algorithmic accountability.

“It is essential that ethics take centre stage in AI’s development and use,” adds committee chairman, Lord Clement-Jones, in a statement. “The UK has a unique opportunity to shape AI positively for the public’s benefit and to lead the international community in AI’s ethical development, rather than passively accept its consequences.”

The report also calls for the government to take urgent steps to help foster “the creation of authoritative tools and systems for auditing and testing training datasets to ensure they are representative of diverse populations, and to ensure that when used to train AI systems they are unlikely to lead to prejudicial decisions” — recommending a publicly funded challenge to incentivize the development of technologies that can audit and interrogate AIs.

“The Centre for Data Ethics and Innovation, in consultation with the Alan Turing Institute, the Institute of Electrical and Electronics Engineers, the British Standards Institute and other expert bodies, should produce guidance on the requirement for AI systems to be intelligible,” the committee adds. “The AI development sector should seek to adopt such guidance and to agree upon standards relevant to the sectors within which they work, under the auspices of the AI Council” — the latter being a proposed industry body it wants established to help ensure “transparency in AI”.

The committee is also recommending a cross-sector AI Code to try to steer developments in a positive, societally beneficial direction — though not for this to be codified in law (the suggestion is it could “provide the basis for statutory regulation, if and when this is determined to be necessary”).

Read the source article at TechCrunch.

5 Trends That Will Dominate 2018 as AI Scales Up

2017 saw an explosion of machine learning in production use, with even deep learning and artificial intelligence (AI) being leveraged for practical applications. “Basic analytics are out; machine learning (and beyond) are in,” says Kenneth Sanford, U.S. lead analytics architect for collaborative data science platform Dataiku, as he looks back on 2017. Sanford says practical […]

2017 saw an explosion of machine learning in production use, with even deep learning and artificial intelligence (AI) being leveraged for practical applications.

“Basic analytics are out; machine learning (and beyond) are in,” says Kenneth Sanford, U.S. lead analytics architect for collaborative data science platform Dataiku, as he looks back on 2017.

Sanford says practical applications of machine learning, deep learning, and AI are “everywhere and out in the open these days,” pointing to the “super billboards” in London’s Piccadilly Circus that leverage hidden cameras gathering data on foot and road traffic (including the make and model of passing cars) to deliver targeted advertisements.

So where will these frameworks and tools take us in 2018? We spoke with a number of IT leaders and industry experts about what to expect in the coming year.

Enterprises will operationalize AI

AI is already here, whether we recognize it or not.

“Many organizations are using AI already, but they may not refer to it as ‘AI,'” says Scott Gnau, CTO of Hortonworks. “For example, any organization using a chatbot feature to engage with customers is using artificial intelligence.”

But many of the deployments leveraging AI technologies and tools have been small-scale. Expect organizations to ramp up in a big way in 2018.

“Enterprises have spent the past few years educating themselves on various AI frameworks and tools,” says Nima Negahban, CTO and co-founder of Kinetica, a specialist in GPU-accelerated databases for high-performance analytics. “But as AI goes mainstream, it will move beyond small-scale experiments to being automated and operationalized. As enterprises move forward with operationalizing AI, they will look for products and tools to automate, manage, and streamline the entire machine learning and deep learning life cycle.”

Negahban predicts 2018 will see an increase in investments in AI life cycle management, and technologies that house the data and supervise the process will mature.

AI reality will lag the hype once again

Ramon Chen, chief product officer of master data management specialist Reltio, is less sanguine. Chen says there have been repeated predictions for several years that tout potential breakthroughs in the use of AI and machine learning, but the reality is that most enterprises have yet to see quantifiable benefits from their investments in these areas.

Read the source article at CIO.com.