Where are we going?

March is Women’s History Month and I wanted to honor it by sharing some insights from Kathryn Hume, President at Fast Forward Labs (a machine learning research and advising company). We spoke about AI’s potential to perpetuate inequality, and I included some things that I learned from her below.

On 4/5, I’ll be in New York for the Future Labs AI Summit. Kathryn Hume, and the top minds in AI at Facebook and IBM Watson, will be speaking about the impact artificial intelligence is having on society, business, and entrepreneurship. I hope I get to see you there! Register for the AI Summit here.

“[Tech news] talks a lot about AI and the social evils it could cause. At a fundamental level, the most popular machine learning techniques use traces of past activity that a human has carried out. We use these traces to train models that spit out predictions. The fact is, algorithms aren’t creative. People have been biased in past. The models pick up traces of past judgement, and perpetuate biases.”

Headlines about machine learning algorithms that resurrect racist housing policies, perpetuate unfair neighborhood policing, and predict future criminals by facial features are chilling. Supervised learning, one of the more popular machine learning tasks, uses pairs of labeled input data and desired output values to discover new things. An example label for input data could be “black female,” with an output value of “likely to commit future crime.” Imagine a court system that depends on a machine learning algorithm to issue sentences. If the algorithm is fed with data made possible by decades of racist policies, the court system will preserve discrimination.

“Here’s the positive flip side: as engineers, when we know bias is baked in, we can make a proactive choice to counter it. It’s almost like affirmative action can take place at scale. One person at the company built FairML, which is basically a tool for auditing black box algorithms.”

Removing biases from algorithms that are used for employment screening, ad-targeting, policing, and credit-worthiness scoring is possible. The real challenge will come in convincing companies to allow their algorithms to be audited. Many companies use black box (intentionally opaque) algorithms to generate much of their proprietary findings. The FairML auditing tool that Kathryn mentions could be used to assess how discriminatory a model is without exposing the company’s proprietary information.

“In non-regulated industries, there isn’t much push for algorithms that are transparent. There would need to be a consumer uprising or internal company decision to create more transparency. We’ll need to get people and privacy research groups interested in questioning the fairness of algorithms. It would be foolhardy to think a grassroots-type initiative would solve the problem. It’s up for grabs on how this might work. My take is, it takes work from the get go to include diversity in an organization.”

Kathryn makes the point that pressure from consumers, privacy groups, and internal employees will be needed to keep discriminatory models from being baked into a company’s algorithms. That pressure from internal employees will be most potent if the employees span a diverse set of genders, sexual orientations, races, and religious beliefs. The more varied perspectives that are present, the less likely biased data are to make it into algorithms that could harm innocent people.

If you’re interested in learning more from Kathryn and AI leaders at Facebook & IBM Watson how AI will impact our society, check out the Future Labs AI Summit.

Awesome, not awesome.

#Awesome

“YouTube user tests indicate that a [machine learning powered speech-to-text] feature significantly improves the experience of the deaf and hard of hearing. Machine learning is giving people like me that need accommodation in some situations the same independence as others.” — Liat Kaver, YouTube Product Manager who is deaf. Learn More on Business Insider >

#God help us all

“Not at all worried. In terms of artificial intelligence taking over American jobs, I think we’re so far away from that that it’s not even on my radar screen. I think it’s 50 or 100 more years…’When pressed, Mnuchin said that he wasn’t talking about things like self-driving cars, which he believes could run from coast to coast in the not-so-distant future.’ That to me isn’t artificial intelligence, that’s computers and using real technology we have today. But those types of things are very real. That’s very different from artificial [intelligence], you know, R2-D2 taking over your job.” — Steven Mnuchin, Treasury Secretary. Learn More on The Verge >

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What we’re reading.

1/ 20 autonomous robots are driving around Washington, D.C., delivering food from restaurants, so people literally never have to leave their homes again.

2/ Autonomous vehicles will be central to improving the quality of life for elderly people: taking them to the doctors office and grocery store so they can live in the comfort of their own homes for longer.

3/ What could possibly go wrong when an autonomous car allows a human to take a nap behind the wheel?

4/ Andrew Ng, one of the leading minds in AI, left his job this week and the world’s largest companies are chasing after him with “NFL quarterback money.”

5/ 38% of US jobs are at high risk of being automated over the next 15 years.

6/ IBM builds AI-enabled products that help non-technical people accomplish complex tasks without any prior training.

7/ A team of Australian scientists created an autonomous robot that swims through coral reefs, detects and executes invasive creatures with lethal injections.

Links from the community.

“A New Point-and-Click Revolution Brings AI To The Masses” submitted by Keith Swiader (@keithswiader). Learn More on Fast Company >

“IBM Speech Recognition System Beats Microsoft’s Less Than Five Months Later” submitted by Jay Zaveri (@superjz). Learn More on TechBooky >

“Five Distractions in Thinking about AI” by Kathryn Hume (@HumeKathryn). Learn More on As Near As May Be >

“Before you Build Another Machine-Learning Startup, Read This” by Preeti Rathi. Learn More on VentureBeat >

“Machine Learning: An In-Depth Guide — Model Performance and Error Analysis” by Alex Castrounis (@InnoArchiTech). Learn More on InnoArchiTech >

“Outsource Your Boring Back Office Paperwork to AI” by Marlene Jia (@mjia). Learn More on TOPBOTS >

Artificial Intelligence & Machine Learning will radically change the way we work and live. Machine Learnings covers the most remarkable news in AI, so you’ll feel prepared for the future.