Joanna Bryson (University of Bath) - Intelligence by Design_ Systems engineering for the coming era of AI regulation

Joanna Bryson (University of Bath) - Intelligence by Design_ Systems engineering for the coming era of AI regulation

• 1.
  Joanna J. Bryson Universityof Bath, United Kingdom @j2bryson Intelligence by Design Software engineering for transparency and accountability
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  Who is responsiblefor AI? How do we govern the commercial use of AI? What is AI?
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  Intelligence is thecapacity to do the right thing at the right time – to translate perception into action. Artificial Intelligence is a trait of artefacts, deliberately built to facilitate our intentions. Nothing about intelligence changes responsibility for that deliberate act.
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  Intelligence is computation–atransformation of information. Not math. Computation is a physical process, taking time, energy, & space. Finding the right thing to do at the right time requires search. Cost of search = # of options# of acts (serial computing). Examples: • Any 2 of 100 possible actions = 1002 = 10,000 possible plans. • # of 35-move games of chess > # of atoms in the universe. Concurrency can save real time, but not energy, and requires more space. Quantum saves on space (sometimes) but not energy(?) Omniscience (“AGI”) is not a real threat. No one algorithm can solve all of AI. Not math. Viv Kendon, Durham
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  Humanity’s winning (ecological) strategyexploits concurrency – we share what we know, mining others’ prior search. Now we do this with machine learning.
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  AI is already“super-human” at chess, go, speech transcription, lip reading, deception detection from posture, forging voices, handwriting, & video, general knowledge and memory. This spectacular recent growth derives from using ML to exploit the discoveries (previous computation) both biological and cultural. Becoming more intelligent does not in itself imply becoming more humanlike.
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  Intelligence is aform of computation; AI extends & reuses ours; ML uploads ours 2015 US labor statistics ρ = 0.90 Our implicit behaviour is not our ideal. Ideals are for explicit planning and cooperation. Caliskan, Bryson & Narayanan, 2017
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  Why do wecare about accountability and transparency?
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  Accountability Transparency Information that letsyou know what your system is doing. Not trust – knowledge (requires cybersecurity.) Responsibility assigned by a society. Manufacturers of commercial products are accountable for what their products do, unless they can prove another actor is (e.g. a user.) Transparency allows corporations to demonstrate due diligence – lack of accountability.
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  • Law andJustice are more about dissuasion than recompense. • Safe, secure, accountable software systems are modular – suffering* in such is incoherent. *e.g. systemic dysphoria of isolation, loss of status or wealth. • No penalty of law enacted directly against an artefact (including a shell company) can have efficacy. Only Humans Can Be Accountable Bryson, Diamantis & Grant (AI & Law, September 2017)
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  1. Robots aremulti-use tools. Robots should not be designed solely or primarily to kill or harm humans, except in the interests of national security. 2. Humans, not robots, are responsible agents. Robots should be designed & operated as far as is practicable to comply with existing laws & fundamental rights & freedoms, including privacy. 3. Robots are products. They should be designed using processes which assure their safety and security. [devops] 4. Robots are manufactured artefacts.They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent. 5. The person with legal responsibility for a robot should be attributed. [like automobile titles] Boden et al 2011; cf. Bryson AISB 2000; Bryson; Prescott, Connection Science, 2017; Floridi 2018. UK Principles of Robotics (2011) Asimov’s Laws revised for Manufacturer Responsibility Owner / Operator Respon- sibility
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  OECD Principles ofAI 2019 (endorsed by 42 governments) 1. AI should benefit people and the planet by driving inclusive growth, sustainable development and well-being. 2. AI systems should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards –  for example, enabling human intervention where necessary – to ensure a fair and just society. 3. There should be transparency and responsible disclosure around AI systems to ensure that people understand when they are engaging with them [the AI systems] and can challenge outcomes. 4. AI systems must function in a robust, secure and safe way throughout their lifetimes, and potential risks should be continually assessed and managed. 5. Organisations and individuals developing, deploying or operating AI systems should be held accountable for their proper functioning in line with the above principles.
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  OECD Principles ofAI 2019 (endorsed by 42 governments ) 1. AI should benefit people and the planet by driving inclusive growth, sustainable development and well-being. 2. AI systems should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards –  for example, enabling human intervention where necessary – to ensure a fair and just society. 3. There should be transparency and responsible disclosure around AI systems to ensure that people understand when they are engaging with them [the AI systems] and can challenge outcomes. 4. AI systems must function in a robust, secure and safe way throughout their lifetimes, and potential risks should be continually assessed and managed. 5. Organisations and individuals developing, deploying or operating AI systems should be held accountable for their proper functioning in line with the above principles.
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  The Design ofIntelligence
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  No intelligent systemsprings into existence from a concept.
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  Google uses onlyits own fiberoptic network (laid globally), chips designed and built in-house (unlike the EU), because of cybersecurity – even other fiberoptic cables in a bundle might spy on traffic. AI is much more than algorithms or data. You cannot separate the social concerns of AI from cybersecurity. Google converts old paper mills, decommissioned coal plants into data centres.
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  Design and Accountability •AI facilitates mandating transparently-honest accounts of system engineering, both development and performance, including human participants’. • Log and monitor who does what, when, in terms of: • Adding or changing lines of code • What data & software libraries are used, their provenance & pedigree. • Training and testing procedures, both during development and in active use. • The active system’s performance.
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  Feasibility of AI(∋ ML ∋ DNN) Transparency • Worst case: AI is as inscrutable as humans. • We audit accounts, not accountant’s synapses. • Systems developers can set up (AI & human) processes to monitor limits on performance. • For decades we’ve trained simpler models to inspect complex models (see recently Ghahramani); transparent models can be better, and easier to improve (see Rudin).
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  facebook – Rapid Releaseat Massive Scale Chuck Rossi https://code.facebook.com/posts/270314900139291/rapid-release-at-massive-scale Monitors are simpler, better-understood AI programs that run continuously to ensure a system is meeting performance metrics, and to detect early evidence of failures or interference / intrusion. Think also about every driverless car fatality. Autombiles are regulated because we know they’re dangerous.
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  • Based onmodular systems-engineering methodologies: Behavior- Based AI (Brooks 1991), Object-Oriented Design (Parnas &al. 1985), and Agile Development (Beck 2000). • Decompose modular systems around what needs to be known in order to perform the systems’ tasks. • Optimise and constrain both perception and learning to subtask. • Provide API to exploit knowledge, derive action from knowledge. • Set priorities of system – must both maintain currency of its awareness / knowledge, and pursue its goals (POSH “plans” = Behaviour Trees.) Behaviour Oriented Design (Bryson 2001)
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  Wortham,Theodorous, & Bryson, RO-MAN2017 video at 5x Transparency for developers via real time visualised priorities ABOD3: A BOD Environment, III (Theodorous)
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  video: live: Wortham,Theodorou & Bryson2017 (exp 1 video) ABOD3 also helps naïve users
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  Wortham PhD (2018) Anthropomorphising may reducetransparency. New research project (funded by 2017 AXA award)
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  What Makes UsResponsible? “Isn’t this a double standard?”
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  • The claimthat purely synthetic intelligent systems shouldn’t themselves require moral consideration is often seen as unfair. • First Answer: Not a double standard, a recommendation. Pick criteria for personhood, don’t build to it, because owning and selling persons is immoral (Bryson 2010, 2018). • Second Answer: Human ethics, even aesthetics, coevolved with our organism. Extending it even to other biological species is difficult, even though they share our qualia. Building AI that we can extend it to is probably impossible and almost certainly immoral (e.g. would require human cloning, Bryson &al. 2017). • Third Answer: A discontinuity occurred when we {evolved | invented} words for the concept of responsibility (Bryson 2008, 2009).
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  • Third Answer:A discontinuity occurred when we {evolved | invented} words for the concept of responsibility (Bryson 2008, 2009).
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  • Third Answer:A discontinuity occurred when we {evolved | invented} words for the concept of responsibility (Bryson 2008, 2009). Implies Ethics Are Not Universal • Proposition: the core of ethics is who is responsible for what. • Moral agents are considered responsible for their actions by a society. • Moral patients are considered the responsibility of a society’s agents. • Proposition: ethics is the set of behaviours that creates and sustains a society, including by defining its identity. • We want to say e.g.“We are more ethical.” Instead, we have to name an ethics metric, e.g.“our society is more ethical in terms of the proportion of the population sharing economic benefits.”
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  What about robots’ phenomenological experiences? Weare obliged to build AI we are not obliged to. We will never build something from chips and wires that shares our visceral experience as much as cows (or rats) do. What about cows’? Bryson, 2010
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  Should we regulateAI? • Yes – we already do. All commerce is regulated. • We just need to do it better – regulatory bodies needed that understand software / DevOps. • Expect those who build and use AI to be accountable, to be able to prove due diligence. • Work with and innovate governments to ensure adequate redistribution (investment in infrastructure).
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  Aylin Caliskan @aylin_cim Thanks to mycollaborators, and to you for your attention. Andreas Theodorou @recklessCoding Tom Dale Grant Mihailis E. Diamantis ... and the rest of Amoni Holly Wilson @wilsh010 Nolan McCarty @nolan_mc Alex Stewart @al_cibiades Arvind Narayanan @random_walker
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  Regulating AI • Donot reward corporations by capping liabilities when they fully automate business processes – Legal lacuna of synthetic persons (Bryson, Diamantis & Grant 2017.) • Do not motivate obfuscation of systems by reducing liabilities for badly-tested or poorly-monitored learning, or special status for systems with ill-defined properties, such as ‘consciousness’. • Clear code is safer and can be more easily maintained, but messy code is cheaper to produce (in the short run.) • Regulation should motivate clarity (transparency, safety) by requiring proof of due diligence.
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  Summary and Conclusions •AI is by definition an artefact – something deliberately built. That deliberation has ethical consequences. • Augmenting our intelligence through technology defines our societies, has lead to our ecological domination. • We are obliged to each other to design AI accountably. • We are obliged not to build AI we are obliged to. • Assuming we deem as ethical an equitable society, sufficiently stable to build businesses and families.
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  Social Disruption from AI/ ICT • Empowerment of individuals. • Rapid formation of new social identities. • Dissipation of distance leading to: • communication of wealth and power across national borders. • concentration of wealth / business ⟹ inequality
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  Inequality Matters Empirically, Gini =.27 ~ideal. 0 is too low, (need to reward excellence); .3–.4 social disruption; > .4 economies decline. ∑ n i=1 ∑ n j=1 |xi − xj | 2n∑ n i=1 xi The Gini Coefficient is half of the relative mean absolute difference in wealth.
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  We can fixthis. Polarization and the Top 1% r = .67 Polarization lagged 12 years r = .91 .5.6.7.8.911.1 Polarization Index 791113151719 Percentage Share 1913 1921 1929 1937 1945 1953 1961 1969 1977 1985 1993 2001 2009 Income share of top 1% Polarization index Figure 1.2: Top One Percent Income Share and House Polarization Voorheis, McCarty & Shor State Income Inequality and Political Polarization We’ve Been Here Before Scheidel, 2017 Polarization over 140 Years r = .89 .2.4.6.811.21877 1885 1893 1901 1909 1917 1925 1933 1941 1949 1957 1965 1973 1981 1989 1997 2005 2013 House Senate Polarization Voorheis, McCarty & Shor State Income Inequality and Political Polarization • Late 19C inequality perhaps driven by then-new distance-reducing technologies: news, oil, rail, telegraph; now bootstrapped by ICT? • Great coupling – period of low inequality where wages track productivity – probably due to policy.
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  What about jobs?
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  AI, Employment, and Wages •We have more AI than ever, & more jobs than ever (Autor, 2015, “Why are there still so many jobs.”) • AI may be increasing inequality, by making it easier to acquire skills. This reduces an aspect of wage differentiation – a factor believed to benefit redistribution. • Example 1: There are more human bank tellers since ATMs, because each branch has fewer, so branches are cheaper, so more branches. • Tellers are now better paid, but fewer branch managers, who used to be really well paid. • Example 2: There aren’t enough truck drivers, because it’s no longer a well-paid job. • GPS + power steering = anyone can do it.