The following is a guest post by Erin Green, PhD, a Brussels-based AI ethics and public engagement specialist. For more on the European scene, check out my recent interview with Hill + Knowlton Strategies “Creating Ethical Rules for AI.”
When it comes to the global AI stage, China and the US consistently grab headlines as their so-called arms race heats up, while countries like Japan and South Korea lead the way in innovation and social receptivity. Europe, though, is taking a slightly different approach – partly by choice, partly by design.
The 28 countries (Brexit pending) that make up the economic and political bloc of the European Union each have a stake in the AI game. Bigger, richer players like the UK (pledging 1000 places for PhDs in AI) and Germany (€3 billion invested in the coming years) are sinking eye-widening resources into keeping up with the proverbial Joneses. Smaller nations, like Malta and its not-quite 500,000 people, are turning to foreign investment and partnerships to guarantee a spot in the major leagues.
Somewhat independent of these interests, the EU itself is trying to carve out space in terms of regulatory prowess and in bringing coherence to a rather chaotic European AI scene. Think this is a bureaucratic exercise with not much reach or consequence beyond the Berlaymont? Just remember all those GDPR emails that clogged up your inbox sometime around May 25, 2018. The EU has real regulatory reach.
Introducing his students to the study of the human brain Jeff Lichtman, a Harvard Professor of Molecular and Cellular Biology, once asked: “If understanding everything you need to know about the brain was a mile, how far have we walked?”. He received answers like ‘three-quarters of a mile’, ‘half a mile’, and ‘a quarter of a mile’.
The professor’s response?: “I think about three inches.”
Last month, Lichtman’s quip made it into the pages of a new report by the Royal Society which examines the prospects for neural (or “brain-computer”) interfaces, a hot research area that has seen billions of dollars of funding plunged into it over the last few years, and not without cause. It is projected that the worldwide market for neurotech products – defined as “the application of electronics and engineering to the human nervous system” – will reach as much as $13.3 billion by 2022.
“What’s Hecuba to him, or he to Hecuba,
That he should weep for her?”
The close of Act II Scene ii, and Hamlet questions how the performers in a play about the siege of Troy are able to convey such emotion – feel such empathy – for the stranger queen of an ancient city.
The construct here is complex. A play within a play, sparking a key moment of introspection, and ultimately self doubt. It is no coincidence that in this same work we find perhaps the earliest use of the term “my mind’s eye,” heralding a shift in theatrical focus from traditions of enacted disputes, lovers passions, and farce, to more a more nuanced kind of drama that issues from psychological turmoil.
Hamlet is generally considered to be a work of creative genius. For many laboring in the creative arts, works like this and those in its broader category serve as aspirational benchmarks. Indelible reminders of the brilliant outlands of human creativity.
Now, for the first time in our history, humans have a rival in deliberate acts of aesthetic creation. In the midst of the avalanche of artificial intelligence hype comes a new promise – creative AI; here to relieve us of burdensome tasks including musical, literary, and artistic composition.
Last month, Oscar Schwartz wrote a byline for OneZero with a familiarly provocative headline: “What If an Algorithm Could Predict Your Unborn Child’s Intelligence?”. The piece described the work of Genomic Prediction, a US company using machine learning to pick through the genetic data of embryos to establish the risk of health conditions. Given the title of the article, the upshot won’t surprise you. Prospective parents can now use this technology to expand their domain over the “design” of new offspring – and “cognitive ability” is among the features up for selection.
Setting aside the contention over whether intelligence is even inheritable, the ethical debate around this sort of pre-screening is hardly new. Gender selection has been a live issue for years now. Way back in 2001, Oxford University bioethicist Julian Savulescu caused controversy by proposing a principle of “Procreative Beneficence” stating that “couples (or single reproducers) should select the child, of the possible children they could have, who is expected to have the best life, or at least as good a life as the others, based on the relevant, available information.” (Opponents to procreative beneficence vociferously pointed out that – regrettably – Savulescu’s principle would likely lead to populations dominated by tall, pale males…).
“One child, one teacher, one book, one pen can change the world.”
These are the inspirational words of activist Malala Yousafzai, best known as “the girl who was shot by the Taliban” for championing female education in her home country of Pakistan. This modest, pared-down idea of schooling is cherished by many. There is something noble about it, perhaps because harkens back to the very roots of intellectual enquiry. No tools and no distractions; just ideas and conversation.
Traditionalists may be reminded of the largely bygone “chalk and talk” methods of teaching, rooted in the belief that students need little more than firm, directed pedagogical instruction to prepare them for the world. Many still reminisce about these relatively uncomplicated teaching techniques, but we should be careful not to misread Yousafzai’s words as prescribing simplicity as the optimal conditions for education.
On the contrary, her comments describe a baseline.
There is strong evidence to show that subject-specific experts frequently fall short on their informed judgments. Particularly when it comes to forecasting.
In fact, in 2005 the University of Pennsylvania’s Professor Philip E. Tetlock devised a test for seasoned and respected commentators that found as their level of expertise rose, their confidence also rose – but not their accuracy. Repeatedly, Tetlock’s experts attached high probability to low frequency events in error, relying upon intuitive casual reasoning rather than probabilistic reasoning. Their assertions were often no more reliable than, to quote the experimenter, “a dart throwing chimp.”
I was reminded of Tetlock’s ensuing book and other similar experiments at the Future Trends Forum in Madrid last month; an event that (valiantly) attempts to convene a room full of thought leaders and task them with predicting our future. Specifically, in this case, our AI future.
The #AIShowBiz Summit 3.0 – which took place last month – sits apart from the often dizzying array of conferences vying for the attention of Bay Area tech natives. Omnipresent AI themes like “applications for deep learning”, “algorithmic fairness”, and “the future of work” are set aside in preference for rather more dazzling conversations on topics like “digital humans”, “AI and creativity”, and “our augmented intelligence digital future.”
It’s not that there’s anything wrong with the big reoccuring AI themes. On the contrary, they are front-and-center for very good reason. It’s that there’s something just a little beguiling about this raft of rather more spacey, futuristic conversations delivered by presenters who are unflinchingly “big picture”, while still preserving necessary practical and technical detail.
“How do we get humans to trust in all this AI we’re building?”, asked Affectiva CEO Rana El-Kaliouby, at the prestigious NYT New Work Summit at Half Moon Bay last week. She had already assumed a consensus that trust-building was the correct way to proceed, and went on to suggest that, rather than equipping users and consumers with the skills and tools to scrutinize AI, we should instead gently coax them into placing more unearned faith in data-driven artifacts.
But how would this be accomplished? Well, Affectiva are “on a mission to humanize technology”, drawing upon machine and deep learning to “understand all things human.” All things human, El-Kaliouby reliably informed us, would include our emotions, our cognitive state, our behaviors, our activities. Note: not to sense, or to tentatively detect, but to understand those things in “the way that humans can.”
Grandiose claims, indeed.
This article was originally posted on the RE•WORK blog.
The way people interact with technology is always evolving. Think about children today – give them a tablet or a smartphone and they have literally no problem in figuring out how to work it. Whilst this is a natural evolution of our relationships with new tech, as it becomes more and more ingrained in our lives it’s important to think about the ethical implications. This isn’t the first time I’ve spoken about ethics and AI – I”ve had guests on the Women in AI Podcast such as Cansu Canca from the AI Ethics Lab and Yasmin J. Erden from St Mary’s University amongst others join me to discuss this area, and I even wrote a white paper on the topic which is on RE•WORK’s digital content hub – so it’s something that’s really causing conversation at the moment. Fiona McEvoy, the founder of YouTheData.com, joined me on the podcast back in June to discuss the importance of collaboration in AI to ensure it’s ethically sound. Fiona will be joining us at the Deep Learning Summit in San Francisco this week, so in advance of this, I caught up with her to see what she’s been working on…