If you work for an organization that uses data and artificial intelligence (AI), or if you are a consumer using data and AI-powered services, what do you need to know about data ethics?
Quite a bit, it turns out. The way things are going, it seems like every few days new ethics controversies, followed by new commitments to privacy and fairness, arise from the ways that businesses and government use data. A few examples:
• Voice assistants like Amazon’s Alexa, Siri, and “Hey Google” are everywhere, on smart phones, computers, and smart speakers. Voice commands satisfy more and more of our needs without resorting to keyboards, touch screens, or call centers. But recently one such assistant, while listening in on a family’s private conversations, recorded a conversation without the family’s knowledge and emailed that recording to a family member’s employee.
Deep learning systems, which are the most headline-grabbing examples of the AI revolution—beating the best human chess and poker players, self-driving cars, etc.—impress us so very much in part because they are inscrutable. Not even the designers of these systems know exactly why they make the decisions they make. We only know that they are capable of being highly accurate…on average.
Meanwhile, software companies are developing complex systems for business and government that rely on “secret sauce” proprietary data and AI models. In order to protect their intellectual property rights, and profitability, the developers of these systems typically decline to reveal how exactly their systems work. This gives rise to a tradeoff between profit motive, which enables rapid innovation (something government in particular isn’t known for), and transparency, which enables detection and correction of mistakes and biases. And mistakes do occur…on average.
On the one hand, a lack of transparency in deep learning and proprietary AI models has led to criticism from a number of sources. Organizations like AI Now and ProPublica are surfacing circumstances where a lack of transparency leads to abuses such as discriminatory bias. The EU has instituted regulations (namely GDPR) that guarantee its citizens the right to an appeal to a human being when AI-based decisions are being made. And, last but not least, there is growing awareness that AI systems—including autonomous driving and health care systems—can be invisibly manipulated by those with a motive like fraud or simple mischief. Continue reading “Who Needs Reasons for AI-Based Decisions?”
Part II of The Completely Non-Technical Guide to Machine Learning and AI
My previous post raised the question “what is machine learning and artificial intelligence (AI)?” and answered with a functional definition: computer systems that combine measurements and math to make decisions so complicated that until recently only humans could make them.
Part I: What is Machine Learning? Combining Measurements and Math to Make Predictions
The labels “machine learning” and “artificial intelligence” can be used interchangeably to describe computer systems that make decisions so complicated that until recently only humans could make them. With the right information, machine learning can do things like…
• look at a loan application, and recommend whether a bank should lend the money
• look at movies you’ve watched, and recommend new movies you might enjoy
• look at photos of human cells, and recommend a cancer diagnosis
Machine learning can be applied to just about anything that can be counted/measured, including numbers, words, and pixels in digital photos.
Takeaways from Sam Charrington’s May, 2017 interview with Jennifer Prendki, senior data science manager and principal data scientist for Walmart.com
I am very grateful to Sam Charrington for his TWiML&AI podcast series. So far I have consumed about 70 episodes (~50 hours). Every podcast is reliably fascinating: so many amazing people accomplishing incredible things. It’s energizing! The September 5, 2017 podcast, recorded in May, 2017 at Sam’s Future of Data Summit event, featured his interview with Jennifer Prendki, who at the time was senior data science manager and principal data scientist for Walmart’s online business (she’s since become head of data science at Atlassian). Jennifer provides an instructive window into agile methodology in machine learning, a topic that will become more and more important as machine learning becomes mainstream and production-centric (or “industrialized”, as Sam dubs it). I’ve taken the liberty of capturing key takeaways from her interview in this blog post. (To be clear, I had no part in creating the podcast itself.) If this topic matters to you, please listen to the original podcast – available via iTunes, Google Play, Soundcloud, Stitcher, and YouTube – it’s worth a listen.
Jennifer Prendki was a member of an internal Walmart data science team supporting two other internal teams, the Perceive team and the Guide team, delivering essential components of Walmart.com’s search experience. The Perceive team is responsible for providing autocomplete and spell check to help improve customers’ search queries. The Guide team is responsible for ranking the search results, helping customers find what they are looking for as easily as possible. Continue reading “Lessons in Agile Machine Learning from Walmart”
I decided to share some links to a few of my favorite (mostly recent) articles and videos about #AI, aka artificial intelligence, and #ML, aka machine learning, in a post here. If anyone wants to submit additions, feel free to contribute in the comments below.
Recent overview articles about AI / Machine Learning