{"id":4112,"date":"2017-12-19T16:34:50","date_gmt":"2017-12-19T16:34:50","guid":{"rendered":"https:\/\/athis-consulting.com\/news\/?p=4112"},"modified":"2018-09-17T17:31:05","modified_gmt":"2018-09-17T17:31:05","slug":"can-we-teach-morality-to-machines-three-perspectives-on-ethics-for-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/athis-technologies.com\/news\/society\/2017\/can-we-teach-morality-to-machines-three-perspectives-on-ethics-for-artificial-intelligence\/","title":{"rendered":"Can we teach morality to machines? Three perspectives on ethics for artificial intelligence"},"content":{"rendered":"
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T<\/span>oday, it is difficult to imagine a technology that is as enthralling and terrifying as machine learning. While media coverage and research papers consistently tout the potential of machine learning to become the biggest driver of positive change in business and society, the lingering question on everyone\u2019s mind is: \u201cWell, what if it all goes terribly wrong<\/a>?\u201d<\/p>\n

For years, experts have warned against the unanticipated effects of general artificial intelligence (AI) on society. Ray Kurzweil\u00a0predicts<\/a>\u00a0that by 2029 intelligent machines will be able to outsmart human beings. Stephen Hawking argues that \u201conce humans develop full AI, it will take off on its own and redesign itself at an ever-increasing rate<\/a>\u201d. Elon Musk warns that AI may constitute a \u201cfundamental risk to the existence of human civilization<\/a>\u201d. Alarmist views on the terrifying potential of general AI abound in the media.<\/p>\n

More often than not, these dystopian prophecies have been met with calls for a more ethical implementation of AI systems; that somehow engineers should imbue autonomous systems with a\u00a0sense of ethics<\/a>. According to some\u00a0AI experts<\/a>, we can teach our future robot overlords to tell right from wrong, akin to a \u201cGood Samaritan AI<\/a>\u201d that will always act justly on its own and help humans in distress.<\/p>\n

Although this future is still decades away, today there is\u00a0much uncertainty<\/a>\u00a0as to how, if at all, we will reach this level of general machine intelligence. But what is more crucial, at the moment, is that even the\u00a0narrow AI applications<\/a>that exist today require our urgent attention in the ways in which they are making moral decisions in practical\u00a0day-to-day situations<\/a>. For example, this is relevant when algorithms make decisions about who gets access to loans or when self-driving cars have to\u00a0calculate the value of a human life<\/a>\u00a0in hazardous traffic situations.<\/p>\n

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Moral dilemmas for self-driving cars (Source: MIT Media\u00a0Lab)<\/figcaption><\/figure>\n

Moral problems in everyday\u00a0life<\/h4>\n

Teaching morality to machines is hard because humans can\u2019t objectively convey morality in measurable metrics that make it easy for a computer to process. In fact, it is even questionable whether we, as humans have a sound understanding of morality at all that we can all agree on. In\u00a0moral dilemmas<\/a>, humans tend to rely on gut feeling instead of elaborate cost-benefit calculations. Machines, on the other hand, need explicit and objective metrics that can be clearly measured and optimized.<\/p>\n

For example, an AI player can excel in games with clear rules and boundaries by learning how to optimize the score through repeated playthroughs. After its experiments with\u00a0deep reinforcement learning on Atari video games<\/a>, Alphabet\u2019s DeepMind was able to beat the best human players of\u00a0Go<\/a>. Meanwhile, OpenAI amassed \u201clifetimes\u201d of experiences to\u00a0beat the best human players<\/a>\u00a0at the\u00a0Valve Dota 2 tournament<\/a>, one of the most popular e-sports competitions globally.<\/p>\n

But in real-life situations, optimization problems are vastly more complex. For example, how do you teach a machine to\u00a0algorithmically maximise fairness<\/a>\u00a0or to overcome\u00a0racial and gender biases<\/a>\u00a0in its training data? A machine cannot be taught what is fair unless the engineers designing the AI system have a precise conception of what fairness is.<\/p>\n

This has led some authors to worry that a naive application of algorithms to everyday problems could\u00a0amplify structural discrimination<\/a>\u00a0and reproduce biases in the data they are based on. In the\u00a0worst case<\/a>, algorithms could deny services to minorities, impede people\u2019s employment opportunities or get the wrong political candidate\u00a0elected<\/a>. Some people have argued that the use of AI in politics already had\u00a0disastrous consequences<\/a>.<\/p>\n

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Thinking about new ways to teach robots right from\u00a0wrong.<\/figcaption><\/figure>\n

So what can we do about it? Based on our experiences in machine learning, we believe there are three ways to begin designing more ethically aligned machines with the following guidelines:<\/p>\n

1. Explicitly defining ethical behaviour<\/h4>\n

AI researchers and ethicists need to formulate ethical values as quantifiable parameters. In other words, they need to provide machines with explicit answers and decision rules to any potential ethical dilemmas it might encounter. This would require that humans agree among themselves on the most ethical course of action in any given situation\u200a\u2014\u200aa challenging but not impossible task. For example, Germany\u2019s\u00a0Ethics Commission on Automated and Connected Driving<\/a>\u00a0has recommended to specifically programme ethical values into self-driving cars to prioritize the protection of human life above all else. In the event of an unavoidable accident, the car should be \u201cprohibited to offset victims against one another\u201d. In other words, a car shouldn\u2019t be able to choose whether to kill one person based on individual features, such as age, gender or physical\/mental constitution when a crash is inescapable.<\/p>\n

2. Crowdsourcing human\u00a0morality<\/h4>\n

Engineers need to collect enough data on explicit ethical measures to appropriately train AI algorithms. Even after we have defined specific metrics for our ethical values, an AI system might still struggle to pick it up if there is not enough unbiased data to train the models. Getting appropriate data is challenging, because ethical norms\u00a0cannot be always clearly standardized<\/a>. Different situations require different ethical approaches, and in some situations there may not be a single ethical course of action at all\u200a\u2014\u200ajust think about\u00a0lethal autonomous weapons<\/a>\u00a0that are currently being developed for military applications. One way of solving this would be to crowdsource potential solutions to moral dilemmas from millions of humans. For instance, MIT\u2019s\u00a0Moral Machine<\/a>\u00a0project shows how crowdsourced data can be used to effectively train machines to make better moral decisions in the context of self-driving cars.<\/p>\n

3. Making AI systems more transparent<\/h4>\n

Policymakers need to implement guidelines that make AI decisions with respect to ethics more transparent, especially with regard to ethical metrics and outcomes. If AI systems make mistakes or have undesired consequences, we cannot accept \u201cthe algorithm did it<\/a>\u201d as an adequate excuse. But we also know that demanding full algorithmic transparency is technically untenable (and, quite frankly, not very useful). Neural networks are simply\u00a0too complex<\/a>to be scrutinized by human inspectors. Instead, there should be more transparency on how engineers quantified ethical values before programming them, as well as the outcomes that the AI has produced as a result of these choices. For self-driving cars, for instance, this could imply that detailed logs of all automated decisions are kept at all times to ensure their ethical accountability.<\/p>\n

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How can moral values be measured and optimised?<\/figcaption><\/figure>\n

Next steps for moral\u00a0machines<\/h4>\n

We believe that these three recommendations should be seen as a starting point for developing ethically aligned AI systems. Failing to imbue ethics into AI systems, we may be placing ourselves in the dangerous situation of allowing algorithms to decide what\u2019s best for us. For example, in an unavoidable accident situation, self-driving cars will need to make some decision for better or worse. But if the car\u2019s designers fail to specify a set of ethical values that could act as decision guides, the AI system may come up with a solution that causes more harm.<\/p>\n

This means that we cannot simply refuse to quantify our values. By walking away from this critical ethical discussion, we are making an implicit moral choice. And as machine intelligence becomes\u00a0increasingly pervasive<\/a>\u00a0in society, the price of inaction could be enormous\u200a\u2014\u200ait could negatively affect the lives of billions of people.<\/p>\n

Machines cannot be assumed to be inherently capable of behaving morally. Humans must teach them what morality is, how it can be measured and optimised. For AI engineers, this may seem like a daunting task. After all, defining moral values is a challenge mankind has struggled with throughout its history. If we can\u2019t agree on what makes a moral human, how can we design moral robots?<\/p>\n

Nevertheless, the state of AI research and its applications in society require us to finally define morality and to quantify it in explicit terms. This is a difficult but not impossible task. Engineers cannot build a \u201cGood Samaritan AI\u201d, as long as they lack a formula for the Good Samaritan human.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

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About the authors:\u00a0<\/em>Jane Zavalishina<\/em><\/a>\u00a0is the CEO of\u00a0<\/em>Yandex Data Factory<\/em><\/a>, a provider of AI-based solutions for industrial companies. Jane is a frequent speaker on the topics of AI business strategy and applications at various events in Europe, Middle East and Asia. She serves on the\u00a0<\/em>World Economic Forum\u2019s Global Future Councils<\/em><\/a>. In 2016, Jane was named in Silicon Republic\u2019s Top 40 Women in Tech as an Inspiring Leader and recognised by Inspiring Fifty as one of the top 50 most inspirational women in the technology sector in the Netherlands.<\/em><\/p>\n

Dr Vyacheslav Polonski<\/em><\/a>\u00a0is a researcher at the\u00a0<\/em>University of Oxford<\/em><\/a>, studying complex social networks and collective behaviour. He holds a PhD in computational social science and has previously studied at Harvard, Oxford and LSE. He is the founder and CEO of\u00a0<\/em>Avantgarde Analytics<\/em><\/a>, a machine learning startup that harnesses AI and behavioural psychology for the next generation of algorithmic campaigns.<\/em>\u00a0Vyacheslav is actively involved in the\u00a0<\/em>World Economic Forum<\/em><\/a>\u00a0<\/em>Expert Network<\/em><\/a>\u00a0and the\u00a0<\/em>WEF Global Shapers<\/em><\/a>\u00a0community, where he served as the Curator of the\u00a0<\/em>Oxford Hub<\/em><\/a>. He writes about the intersection of sociology, network science and technology.<\/em><\/p>\n

Earlier versions of this article were published on the\u00a0<\/em>Net Politics Blog<\/em><\/a>\u00a0of the\u00a0<\/em>Council on Foreign Relations<\/em><\/a>\u00a0on 14 November 2017, the\u00a0<\/em>World Economic Forum Agenda<\/em><\/a>\u00a0on 23 November 2017 and the official blog of the\u00a0<\/em>BCG Centre for Public Impact<\/em><\/a>\u00a0on 12 December 2017. The article was also translated into\u00a0<\/em>French<\/em><\/a>\u00a0and\u00a0<\/em>Polish<\/em><\/a>\u00a0in other online media outlets.<\/em><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"

Today, it is difficult to imagine a technology that is as enthralling and terrifying as machine learning. While media coverage and research papers consistently tout the potential of machine learning to become the biggest driver of positive change in business and society, the lingering question on everyone\u2019s mind is: \u201cWell, what if it all goes […]<\/p>\n","protected":false},"author":5,"featured_media":4113,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"amp_status":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"Can we teach morality to machines? 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