“AI: Intelligent Machines, Smart Policies”: three conference takeaways

Clara Young, OECD Public Affairs and Communications Directorate

Listening to the radio this morning, I heard a story about a former FBI agent who had come out of retirement to reopen a very old case: who tipped off the Gestapo to Anne Frank’s whereabouts? There have been two investigations into the circumstances leading up to the arrest of the young diarist and her family on 4 August 1944 in Amsterdam, but this newest attempt is using artificial intelligence (AI). “The artificial intelligence programme will be able to make connections and associations of dates, persons and locations that would take a human investigator a minimum of 10 years to come up with,” lead investigator Vince Pankoke told the Canadian Broadcasting Corporation (CBC).

Artificial intelligence can solve the most intractable of puzzles. But with it come many new, possibly more intractable, questions. At a recent OECD conference “AI: Intelligent Machines, Smart Policies”, researchers, economists, policymakers, advisors, and labour and corporate representatives came to grips with the vastly different landscape AI is beginning to create. With their algorithmic ability to navigate through the noise of big data, machine-learning AI robots are commonplace in biotech labs. They formulate scientific hypotheses, devise and conduct experiments, and analyse test results, probing deeply and around the clock. AI can pilot vehicles, determine what your car insurance premium should be, detect malicious cyberactivity, improve medical diagnoses through image recognition like radiography and ultrasonography, and even compose music.

But will such tremendous computational and learning capacities upend human society? Stuart W. Elliott, who is Director of Board on Testing and Assessment at the US National Academy of Science, observes that AI currently has literal and numerical levels that are as good as if not better than 89% of adults in OECD countries. What implications does that have for competition in the labour market? How can policy makers and legislators plan for the magnitude of labour disruption automisation will bring?

Another conference takeaway is the need for transparency in AI decision-making. When software is making decisions on whether, for example, a driverless car should swerve away from an oncoming bicyclist and hit a pedestrian on the sidewalk, or if a job applicant should be hired or rejected, people should be able to look at the chain of reasoning leading up to an AI decision. There is also the concern that the algorithms in AI software distort natural biases implicit in data. For instance, Science reported that tests have shown that machine learning software absorbs societal racial biases in data, and makes stereotyped associations between European American names and positive or pleasant terms, and African-American names and negative or unpleasant terms. A related study showed that job applicants with European American names were 50% more likely to be accorded an interview by AI software.

But perhaps the biggest preoccupation at the conference is the data conundrum. In a forthcoming OECD interview, Dudu Mimran, CTO of Telekom Innovation Laboratories and Cyber Security Research Center at Ben-Gurion University in Israel, described the current data environment as the “…Wild West with all companies collecting any data”. Data is used to train artificial intelligence, and the more of it the better. But do we always know where it is coming from? And, who owns it? Digital advisor to the Estonian government Marten Kaevats stood up during a panel discussion and said, “The people own their own data.” In embracing digitalised government so early on, Estonia may be considered as a leader on data issues. Its citizens’ health and tax records are online, protected by a closed blockchain system. Online voting was introduced in 2005. But outside such digitally advanced regimes, most people do not know where their personal data reside, how it is being used, and whether its integrity is being safeguarded. One example of data carelessness is the discovery in 2016 that the UK’s National Health Service had given Google-owned AI company DeepMind access to the healthcare data of 1.6 million patients without adequately informing them.  

Safeguards exist against such errors. These include the 1980 OECD Privacy Guidelines revised in 2013, the EU’s General Data Protection Regulation, which comes into effect in 2018, and the 2016 signing of the US-EU data protection “Umbrella Agreement” which governs data-sharing in criminal investigations. But, AI raises potentially new and specific privacy risks that may not be covered by these data protection regulations and agreements.

In the case of Anne Frank, the data surrounding her and her family’s capture is 73 years old. Privacy is no longer an issue. For the rest of us, however, the ever-broadening and creative reach of data mining requires vigilance.

References and links

Bohan, John (2017), “A new breed of scientist, with brains of silicon”, Science. See: www.sciencemag.org/news/2017/07/new-breed-scientist-brains-silicon.

Hodson, Hal (2016), “Revealed: Google AI has access to huge haul of NHS patient data”, New Scientist. See https://www.newscientist.com/article/2086454-revealed-google-ai-has-access-to-huge-haul-of-nhs-patient-data.

Elliott, Stuart W. (2017), “Artificial intelligence and the future of work and skills: will this time be different?” at: https://www.oecd-forum.org/channels/722-digitalisation/posts/21601-artificial-intelligence-and-the-future-of-work-and-skills-will-this-time-be-different.

European Commission (2016), “Signing of the ‘Umbrella’ Agreement: A major step forward in EU-U.S. relations”, Brussels. See: http://ec.europa.eu/justice/newsroom/data-protection/news/160602_en.htm.

Caliskan, Aylin; Bryson, Joanna J.; Narayanan, Arvind, “Semantics derived automatically from language corpora contain human-like biases”, Science, 14 Apr 2017: Vol. 356, Issue 6334. See: http://science.sciencemag.org/content/356/6334/183.

Digital Economy Outlook 2017: What artificial intelligence really means for policy makers

Wonki Min, Leading Professor, Department of Technology & Society, SUNY (The State University of New York) Korea, and Chair of the OECD Committee on Digital Economy Policy

In October 2016, “Westworld” topped the charts as the most-watched premiere season of an HBO original series ever. In the series, a science fiction thriller written and directed by novelist Michael Crichton based on a 1973 film of the same name, Anthony Hopkins takes on the role of Dr Ford, who creates a futuristic western-themed amusement park populated by android hosts to cater human guests, with Evan Rachel Wood playing the role of Dolores, the oldest android host working in the park. Further to the great script and the impressive casting, the success of the series is also undoubtedly linked to its timing. Just one year ago, Lee Sedol, 18-time world Go-board game champion, was beaten by DeepMind’s AlphaGo, which was a monumental breakthrough of Artificial Intelligence (AI).

Even before the AlphaGo’s victory over Lee Sedol, there was growing interest in the potential and risks of humanoid robots and of AI, led by the likes of Stephen Hawking and Elon Musk. While the debate remains open on whether, or when, it might be possible to develop the artificial intelligence of Westworld-type humanoid robots, the use of industrial robots (essentially motor functions) and the appearance of autonomous machines with cognitive-type functions have been growing rapidly and raise concerns about job displacements by automation in the manufacturing sectors, including for vehicles.

As countries and international communities grapple with this question and the consequences of the rapid diffusion of AI and industrial robots, the 2017 OECD Digital Economy Outlook takes a timely look at the potential benefits and opportunities offered by AI as it begins to take hold and slowly penetrate, if not disrupt and transform, our economies and societies.

Productivity gains could be achieved in areas ranging from factories to offices and service centres as a result of both the automation of activities previously carried out by people and machine autonomy whereby systems are able to operate and adapt to changing circumstances with no or little human control. But as AI and robotics replace or augment components of human labour in both skilled and unskilled jobs, policies will be required to facilitate professional transitions and to help workers of various backgrounds, ages and levels develop the skills needed to take full advantage of the digital transformation.

Skills are not the only challenge for policy makers to address, as this new report shows: we need to provide access and connectivity, measures to promote innovation, and policies to enhance digital security and trust. Indeed new questions of liability, responsibility, security and safety also come up, notably with respect to autonomous machines. AI-powered decisions that impact people raise questions of transparency and oversight, among other things to prevent algorithmic biases, discrimination and privacy abuses. There is some urgency to act on these policy fronts, since the data show this new revolution to be well under way.

Roughly 750 000 industrial robots were estimated operational in OECD countries in 2014, constituting more than 80% of world stocks. Among OECD countries, Japan, the US, Korea and Germany are the most “robotised” countries in the OECD and together account for almost 70% of the total number of operational robots. In terms of the adoption of industrial robots by sector, the use of industrial robots is the most highly concentrated in transport equipment with almost 45% of the total stock of robots, followed by electronic, electrical and optical equipment, with almost 30%. Rubber and plastics have lower concentration at less than 10% and metal products (5%).

As for the future, expect to see more and more industrial robots replacing human workers in manufacturing facilities along with collaborative robots like “Baxter” that co-work with human workforce and service robots like “Relay”, a robot waiter that delivers food and snacks to guest at Shinagawa Prince Hotel in Tokyo. With AI, expert and high-wage occupations will also be impacted.

In the meantime, the use of AI and industrial robots will no doubt bring new opportunities to raise incomes, create new types of jobs and businesses and improve economic and social well-being. But there will be costs and bumps along the way. It is up to policy makers to play their part by helping make the digital transformation beneficial for all.

References

OECD (2017), OECD Digital Economy Outlook 2017, OECD Publishing, Paris.
http://dx.doi.org/10.1787/9789264276284-en 

OECD (2017), The Next Production Revolution: Implications for Governments and Business, OECD Publishing, Paris.
http://dx.doi.org/10.1787/9789264271036-en