The recent news that Amazon inadvertently created gender-biased software for screening job applicants is a significant wake-up call for all organizations using AI. The software, which used machine learning to rank incoming resumes by comparison to resumes from people Amazon had already hired, could have discouraged recruiters from hiring women solely on the basis of their gender. Amazon, of all entities, should have known better. It should have expected and avoided this. If this can happen to Amazon, the question we really need to ask is: how many others are making the same mistake?
Be it the core of their product, or just a component of the apps they use, every organization is adopting machine learning and AI at some level. Most organizations are adopting it an ad hoc fashion, but there are a number of considerations—with significant potential consequences for cost, timing, risk, and reward—that they really should consider together.
That’s why I developed the following framework for organizations planning to adopt machine learning or wanting to take their existing machine learning commitment to the next level.
Last week, at a farewell party for a data scientist friend (who is about to ship out from Seattle to Palo Alto to work for a certain social media network based there), I had an interesting exchange with another friend who runs a self funded AI-based startup. Our conversation turned to wondering about whether we’re in the middle of an AI bubble (remember the dotcom bubble?). He asked whether I thought there would be any winners if the AI bubble bursts, and my answer was as follows.
I recently had a series of conversations about how the public perceives two brands that I find intriguing: Bob’s Red Mill, a natural foods producer based in Oregon, and Hyperloop, a platform for rapid long distance transportation that is being implemented by a number of organizations all over the world. I took some notes—and created this post.
To me, the common thread between Bob’s Red Mill and Hyperloop is that they both let the people behind them represent them. It makes their value propositions credible in a way that clever writing and a huge creative budget can’t.
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.