AI Ethics: Seven Traps

Curated on Posted on by Stefaan Verhulst

Blog Post by Annette Zimmermann and Bendert Zevenbergen: “… In what follows, we outline seven ‘AI ethics traps’. In doing so, we hope to provide a resource for readers who want to understand and navigate the public debate on the ethics of AI better, who want to contribute to ongoing discussions in an informed and nuanced way, and who want to think critically and constructively about ethical considerations in science and technology more broadly. Of course, not everybody who contributes to the current debate on AI Ethics is guilty of endorsing any or all of these traps: the traps articulate extreme versions of a range of possible misconceptions, formulated in a deliberately strong way to highlight the ways in which one might prematurely dismiss ethical reasoning about AI as futile.

1. The reductionism trap:

“Doing the morally right thing is essentially the same as acting in a fair way. (or: transparent, or egalitarian, or <substitute any other value>). So ethics is the same as fairness (or transparency, or equality, etc.). If we’re being fair, then we’re being ethical.”

            Even though the problem of algorithmic bias and its unfair impact on decision outcomes is an urgent problem, it does not exhaust the ethical problem space. As important as algorithmic fairness is, it is crucial to avoid reducing ethics to a fairness problem alone. Instead, it is important to pay attention to how the ethically valuable goal of optimizing for a specific value like fairness interacts with other important ethical goals. Such goals could include—amongst many others—the goal of creating transparent and explainable systems which are open to democratic oversight and contestation, the goal of improving the predictive accuracy of machine learning systems, the goal of avoiding paternalistic infringements of autonomy rights, or the goal of protecting the privacy interests of data subjects. Sometimes, these different values may conflict: we cannot always optimize for everything at once. This makes it all the more important to adopt a sufficiently rich, pluralistic view of the full range of relevant ethical values at stake—only then can one reflect critically on what kinds of ethical trade-offs one may have to confront.

2. The simplicity trap:

“In order to make ethics practical and action-guiding, we need to distill our moral framework into a user-friendly compliance checklist. After we’ve decided on a particular path of action, we’ll go through that checklist to make sure that we’re being ethical.”

            Given the high visibility and urgency of ethical dilemmas arising in the context of AI, it is not surprising that there are more and more calls to develop actionable AI ethics checklists. For instance, a 2018 draft report by the European Commission’s High-Level Expert Group on Artificial Intelligence specifies a preliminary ‘assessment list’ for ‘trustworthy AI’. While the report plausibly acknowledges that such an assessment list must be context-sensitive and that it is not exhaustive, it nevertheless identifies a list of ten fixed ethical goals, including privacy and transparency. But can and should ethical values be articulated in a checklist in the first place? It is worth examining this underlying assumption critically. After all, a checklist implies a one-off review process: on that view, developers or policy-makers could determine whether a particular system is ethically defensible at a specific moment in time, and then move on without confronting any further ethical concerns once the checklist criteria have been satisfied once. But ethical reasoning cannot be a static one-off assessment: it required an ongoing process of reflection, deliberation, and contestation. Simplicity is good—but the willingness to reconsider simple frameworks, when required, is better. Setting a fixed ethical agenda ahead of time risks obscuring new ethical problems that may arise at a later point in time, or ongoing ethical problems that become apparent to human decision-makers only later.

3. The relativism trap:

“We all disagree about what is morally valuable, so it’s pointless to imagine that there is a universalbaseline against which we can use in order to evaluate moral choices. Nothing is objectively morally good: things can only be morally good relative to each person’s individual value framework.”

            Public discourse on the ethics of AI frequently produces little more than an exchange of personal opinions or institutional positions. In light of pervasive moral disagreement, it is easy to conclude that ethical reasoning can never stand on firm ground: it always seems to be relative to a person’s views and context. But this does not mean that ethical reasoning about AI and its social and political implications is futile: some ethical arguments about AI may ultimately be more persuasive than others. While it may not always be possible to determine ‘the one right answer’, it is often possible to identify at least  some paths of action are clearly wrong, and some paths of action that are comparatively better (if not optimal all things considered). If that is the case, comparing the respective merits of ethical arguments can be action-guiding for developers and policy-makers, despite the presence of moral disagreement. Thus, it is possible and indeed constructive for AI ethics to welcome value pluralism, without collapsing into extreme value relativism.

4. The value alignment trap:

“If relativism is wrong (see #3), there must be one morally right answer. We need to find that right answer, and ensure that everyone in our organisation acts in alignment with that answer. If our ethical reasoning leads to moral disagreement, that means that we have failed.”…(More)”.

The global South is changing how knowledge is made, shared and used

Curated on Posted on by Stefaan Verhulst

Robert Morrell at The Conversation: “Globalisation and new technology have changed the ways that knowledge is made, disseminated and consumed. At the push of a button, one can find articles or sources from all over the world. Yet the global knowledge economy is still marked by its history.

The former colonial nations of the nineteenth and twentieth centuries – the rich countries of Europe and North America which are collectively called the global North (normally considered to include the West and the first world, the North contains a quarter of the world’s population but controls 80% of income earned) – are still central in the knowledge economy. But the story is not one simply of Northern dominance. A process of making knowledge in the South is underway.

European colonisers encountered many sophisticated and complex knowledge systems among the colonised. These had their own intellectual workforces, their own environmental, geographical, historical and medical sciences. They also had their own means of developing knowledge. Sometimes the colonisers tried to obliterate these knowledges.

In other instances colonisers appropriated local knowledge, for instance in agriculture, fisheries and mining. Sometimes they recognised and even honoured other knowledge systems and intellectuals. This was the case among some of the British in India, and was the early form of “Orientalism”, the study of people and cultures from the East.

In the past few decades, there’s been more critique of global knowledge inequalities and the global North’s dominance. There have also been shifts in knowledge production patterns; some newer disciplines have stepped away from old patterns of inequality.

These issues are examined in a new book, Knowledge and Global Power: Making new sciences in the South (published by Wits University Press), which I co-authored with Fran Collyer, Raewyn Connell and Joao Maia. The focus is especially on those areas where old patterns are not being replicated, so the study chooses climate change, gender and HIV and AIDS as three new areas of knowledge production in which new voices from the South might be prominent….(More)”.

How AI Can Cure the Big Idea Famine

Curated on Posted on by Stefaan Verhulst

Saahil Jayraj Dama at JoDS: “Today too many people are still deprived of basic amenities such as medicine, while current patent laws continue to convolute and impede innovation. But if allowed, AI can provide an opportunity to redefine this paradigm and be the catalyst for change—if….

Which brings us to the most befitting answer: No one owns the intellectual property rights to AI-generated creations, and these creations fall into the public domain. This may seem unpalatable at first, especially since intellectual property laws have played such a fundamental role in our society so far. We have been conditioned to a point where it seems almost unimaginable that some creations should directly enter the public domain upon their birth.

But, doctrinally, this is the only proposition that stays consistent to extant intellectual property laws. Works created by AI have no rightful owner because the application of mind to generate the creation, along with the actual generation of the creation, would entirely be done by the AI system. Human involvement is ancillary and is limited to creating an environment within which such a creation can take form.

This can be better understood through a hypothetical example: If an AI system were to invent a groundbreaking pharmaceutical ingredient which completely treats balding, then the system would likely begin by understanding the problem and state of prior art. It would undertake research on causes of balding, existing cures, problems with existing cures, and whether its proposed cure would have any harmful side effects. It would also possibly combine research and knowledge across various domains, which could range from Ayurveda to modern-day biochemistry, before developing its invention.

The developer can lay as much stake to this invention as the team behind AlphaGo for beating Lee Sedol at Go. The user is even further detached from the exercise of ingenuity: She would be the person who first thought, “We should build a Go playing AI system,” and direct the AI system to learn Go by watching certain videos and playing against itself. Despite the intervention of all these entities, the fact remains that the victory only belongs to AlphaGo itself.

Doctrinal issues aside, this solution ties in with what people need from intellectual property laws: more openness and accessibility. The demands for improved access to medicines and knowledge, fights against cultural monopolies, and brazen violations of unjust intellectual property laws are all symptomatic of the growing public discontent against strong intellectual property laws. Through AI, we can design legal systems which address these concerns and reform the heavy handed approach that has been adopted toward intellectual property rights so far.

Tying the Threads Together

For the above to materialize, governments and legislators need to accept that our present intellectual property system is broken and inconsistent with what people want. Too many people are being deprived of basic amenities such as medicines, patent trolls and patent thickets are slowing innovation, educational material is still outside the reach of most people, and culture is not spreading as widely as it should. AI can provide an opportunity for us to redefine this paradigm—it can lead to a society that draws and benefits from an enriched public domain.

However, this approach does come with built-in cynicism because it contemplates an almost complete overhaul of the system. One could argue that if open access for AI-generated creations does become the norm, then innovation and creativity would suffer as people would no longer have the incentive to create. People may even refuse to use their AI systems, and instead stick to producing inventions and creative works by themselves. This would be detrimental to scientific and cultural progress and would also slow adoption of AI systems in society.

Yet, judging by the pace at which these systems have progressed so far and what they can currently do, it is easy to imagine a reality where humans developing inventions and producing creative works almost becomes an afterthought. If a machine can access all the world’s publicly available knowledge and information to develop an invention, or study a user’s likes and dislikes while producing a new musical composition, it is easy to see how humans would, eventually, be pushed out of the loop. AI-generated creations are, thus, inevitable.

The incentive theory will have to be reimagined, too. Constant innovation coupled with market forces will change the system from “incentive-to-create” to “incentive-to-create-well.” While every book, movie, song, and invention is treated at par under the law, only the best inventions and creative works will thrive under the new model. If a particular developer’s AI system can write incredible dialogue for a comedy film or invent the most efficient car engines, the market would want more of these AI systems. Thus incentive will not be eliminated, it will just take a different form.

It is true that writing about such grand schemes is significantly tougher than practically implementing them. But, for any idea to succeed, it must start with a discussion such as this one. Admittedly, we are still a moonshot away from any country granting formal recognition to open access as the basis of its intellectual property laws. And even if a country were to do this, it faces a plethora of hoops to jump through, such as conducting feasibility-testing and dealing with international and internal pressure. Despite these issues, facilitating better access through AI systems remains an objective worth achieving for any society that takes pride in being democratic and equal….(More)”.

Civic Tech for Civic Engagement

Curated on Posted on by Stefaan Verhulst

Blog Post by Jason Farra: “When it came to gathering input for their new Environmental Master Plan, the Town of Okotoks, AB decided to try something different. Rather than using more traditional methods of consulting residents, they turned to a Canadian civic tech company called Ethelo.

Ethelo’s online software “enables groups to evaluate scenarios, apply constraints, prioritize options and come up with decisions that will get broad support from the group,” says John Richardson, the company’s CEO and founder.

Okotoks gathered over 350 responses, with residents able to compare and evaluate different solutions for a variety of environmental issues, including what kind of transportation and renewable energy options they wanted to see in their town.

One of the options presented to Okotoks residents in the online engagement site for the town’s Environmental Master Plan.

“Ethelo offered a different opportunity in terms of allowing a conversation to happen online,” Marni Hutchison, Communications Specialist with the Town of Okotoks, said in a case study of the project. “We can see the general consensus as it’s forming and participants have more opportunities to see different perspectives.”

John sees this as part of a broader shift in how governments and other organizations are approaching stakeholder engagement, particularly with groups like IAP2 working to improve engagement practices by training practitioners.

Rather than simply consulting, then informing residents about decisions, civic tech startups like Ethelo allow governments to involve residents more actively in the actual decision-making process….(More)”.

What Would More Democratic A.I. Look Like?

Curated on Posted on by Stefaan Verhulst

Blog post by Andrew Burgess: “Something curious is happening in Finland. Though much of the global debate around artificial intelligence (A.I.) has become concerned with unaccountable, proprietary systems that could control our lives, the Finnish government has instead decided to embrace the opportunity by rolling out a nationwide educational campaign.

Conceived in 2017, shortly after Finland’s A.I. strategy was announced, the government wants to rebuild the country’s economy around the high-end opportunities of artificial intelligence, and has launched a national programto train 1 percent of the population — that’s 55,000 people — in the basics of A.I. “We’ll never have so much money that we will be the leader of artificial intelligence,” said economic minister Mika Lintilä at the launch. “But how we use it — that’s something different.”

Artificial intelligence can have many positive applications, from being trained to identify cancerous cells in biopsy screenings, predict weather patterns that can help farmers increase their crop yields, and improve traffic efficiency.

But some believe that A.I. expertise is currently too concentrated in the hands of just a few companies with opaque business models, meaning resources are being diverted away from projects that could be more socially, rather than commercially, beneficial. Finland’s approach of making A.I. accessible and understandable to its citizens is part of a broader movement of people who want to democratize the technology, putting utility and opportunity ahead of profit.

This shift toward “democratic A.I.” has three main principles: that all society will be impacted by A.I. and therefore its creators have a responsibility to build open, fair, and explainable A.I. services; that A.I. should be used for social benefit and not just for private profit; and that because A.I. learns from vast quantities of data, the citizens who create that data — about their shopping habits, health records, or transport needs — have a right to say and understand how it is used.

A growing movement across industry and academia believes that A.I. needs to be treated like any other “public awareness” program — just like the scheme rolled out in Finland….(More)”.

PayStats helps assess the impact of the low-emission area Madrid Central

Curated on Posted on by Stefaan Verhulst

BBVA API Market: “How do town-planning decisions affect a city’s routines? How can data help assess and make decisions? The granularity and detailed information offered by PayStats allowed Madrid’s city council to draw a more accurate map of consumer behavior and gain an objective measurement of the impact of the traffic restriction measures on commercial activity.

In this case, 20 million aggregate and anonymized transactions with BBVA cards and any other card at BBVA POS terminals were analyzed to study the effect of the changes made by Madrid’s city council to road access to the city center.

The BBVA PayStats API is targeted at all kinds of organizations including the public sector, as in this case. Madrid’s city council used it to find out how restricting car access to Madrid Central impacted Christmas shopping. From information gathered between December 1 2018 and January 7 2019, a comparison was made between data from the last two Christmases as well as the increased revenue in Madrid Central (Gran Vía and five subareas) vs. the increase in the entire city.

According to the report drawn up by council experts, 5.984 billion euros were spent across the city. The sample shows a 3.3% increase in spending in Madrid when compared to the same time the previous year; this goes up to 9.5% in Gran Vía and reaches 8.6% in the central area….(More)”.

Democracy vs. Disinformation

Curated on Posted on by Stefaan Verhulst

Ana Palacio at Project Syndicate: “These are difficult days for liberal democracy. But of all the threats that have arisen in recent years – populism, nationalism, illiberalism – one stands out as a key enabler of the rest: the proliferation and weaponization of disinformation.

The threat is not a new one. Governments, lobby groups, and other interests have long relied on disinformation as a tool of manipulation and control.

What is new is the ease with which disinformation can be produced and disseminated. Advances in technology allow for the increasingly seamless manipulation or fabrication of video and audio, while the pervasiveness of social media enables false information to be rapidly amplified among receptive audiences.

Beyond introducing falsehoods into public discourse, the spread of disinformation can undermine the possibility of discourse itself, by calling into question actual facts. This “truth decay” – apparent in the widespread rejection of experts and expertise – undermines the functioning of democratic systems, which depend on the electorate’s ability to make informed decisions about, say, climate policy or the prevention of communicable diseases.

The West has been slow to recognize the scale of this threat. It was only after the 2016 Brexit referendum and US presidential election that the power of disinformation to reshape politics began to attract attention. That recognition was reinforced in 2017, during the French presidential election and the illegal referendum on Catalan independence.

Now, systematic efforts to fight disinformation are underway. So far, the focus has been on tactical approaches, targeting the “supply side” of the problem: unmasking Russia-linked fake accounts, blocking disreputable sources, and adjusting algorithms to limit public exposure to false and misleading news. Europe has led the way in developing policy responses, such as soft guidelines for industry, national legislation, and strategic communications.

Such tactical actions – which can be implemented relatively easily and bring tangible results quickly – are a good start. But they are not nearly enough.

To some extent, Europe seems to recognize this. Early this month, the Atlantic Council organized #DisinfoWeek Europe, a series of strategic dialogues focused on the global challenge of disinformation. And more ambitious plans are already in the works, including French President Emmanuel Macron’s recently proposed European Agency for the Protection of Democracies, which would counter hostile manipulation campaigns.

But, as is so often the case in Europe, the gap between word and deed is vast, and it remains to be seen how all of this will be implemented and scaled up. In any case, even if such initiatives do get off the ground, they will not succeed unless they are accompanied by efforts that tackle the demand side of the problem: the factors that make liberal democratic societies today so susceptible to manipulation….(More)”.

Visualizing where rich and poor people really cross paths—or don’t

Curated on Posted on by Stefaan Verhulst

Ben Paynter at Fast Company: “…It’s an idea that’s hard to visualize unless you can see it on a map. So MIT Media Lab collaborated with the location intelligence firm Cuebiqto build one. The result is called the Atlas of Inequality and harvests the anonymized location data from 150,000 people who opted in to Cuebiq’s Data For Good Initiative to track their movement for scientific research purposes. After isolating the general area (based on downtime) where each subject lived, MIT Media Lab could estimate what income bracket they occupied. The group then used data from a six-month period between late 2016 and early 2017 to figure out where these people traveled, and how their paths overlapped.

[Screenshot: Atlas of Inequality]

The result is an interactive view of just how filtered, sheltered, or sequestered many people’s lives really are. That’s an important thing to be reminded of at a time when the U.S. feels increasingly ideologically and economically divided. “Economic inequality isn’t just limited to neighborhoods, it’s part of the places you visit every day,” the researchers say in a mission statement about the Atlas….(More)”.

Public Interest Technology University Network

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About: “The Public Interest Technology Universities Network is a partnership that fosters collaboration between 21 universities and colleges committed to building the nascent field of public interest technology and growing a new generation of civic-minded technologists. Through the development of curricula, research agendas, and experiential learning programs in the public interest technology space, these universities are trying innovative tactics to produce graduates with multiple fluencies at the intersection of technology and policy. By joining PIT-UN, members commit to field building on campus. Members may choose to focus on some or all of these elements, in addition to other initiatives they deem relevant to establishing public interest technology on campus:

  1. Support curriculum and faculty development to enable interdisciplinary and cross-disciplinary education of students, so they can critically assess the ethical, political, and societal implications of new technologies, and design technologies in service of the public good.
  2. Develop experiential learning opportunities such as clinics, fellowships, apprenticeships, and internship, with public and private sector partners in the public interest technology space.
  3. Find ways to support graduates who pursue careers working in public interest technology, recognizing that financial considerations may make careers in this area unaffordable to many.
  4. Create mechanisms for faculty to receive recognition for the research, curriculum development, teaching, and service work needed to build public interest technology as an arena of inquiry.
  5. Provide institutional data that will allow us to measure the effectiveness of our interventions in helping to develop the field of public interest technology….(More)”.

The trouble with informed consent in smart cities

Curated on Posted on by Stefaan Verhulst

Blog Post by Emilie Scott: “…Lilian Edwards, a U.K.-based academic in internet law, points out that public spaces like smart cities further dilutes the level of consent in the IoT: “While consumers may at least have theoretically had a chance to read the privacy policy of their Nest thermostat before signing the contract, they will have no such opportunity in any real sense when their data is collected by the smart road or smart tram they go to work on, or as they pass the smart dustbin.”

If citizens have expectations that their interactions in smart cities will resemble the technological interactions they have become familiar with, they will likely be sadly misinformed about the level of control they will have over what personal information they end up sharing.

The typical citizen understands that “choosing convenience” when you engage with technology can correspond to a decrease in their level of personal privacy. On at least some level, this is intended to be a choice. Most users may not choose to carefully read a privacy policy on a smartphone application or a website; however, if that policy is well-written and compliant, the user can exercise a right to decide whether they consent to the terms and wish to engage with the company.

The right to choose what personal information you exchange for services is lost in the smart city.

Theoretically, the smart city can bypass this right because municipal government services are subject to provincial public-sector privacy legislation, which can ultimately entail informing citizens their personal information is being collected by way of a notice.

However, the assumption that smart city projects are solely controlled by the public sector is questionable and verges on problematic. Most smart-city projects in Canada are run via public-private partnerships as municipal governments lack both the budget and the expertise to implement the technology system. Private companies can have leading roles in designing, building, financing, operating and maintaining smart-city projects. In the process, they can also have a large degree of control over the data that is created and used.

In some countries, these partnerships can even result in an unprecedented level of privatization. For example, Cisco Systems debatably has a larger claim over Songdo’s development than the South Korean government. Smart-city public-private partnership can have complex implications for data control even when both partners are highly engaged. Trapeze, a private-sector company in transportation software, cautions the public sector on the unintended transfer of data control when electing private providers to operate data systems in a partnership….

When the typical citizen enters a smart city, they will not know 1.) what personal information is being collected, nor will they know 2.) who is collecting it. The former is an established requirement of informed consent, and the later has debatably never been an issue until the development of smart cities.

While similar privacy issues are playing out in smart cities all around the world, Canada must take steps to determine how its own specific privacy legal structure is going to play a role in responding to these privacy issues in our own emerging smart-city projects….(More)”.