artificial intelligence

The Biggest Hope for Ending Corruption Is Open Public Contracting

Curated on May 11, 2016August 3, 2018 by Stefaan Verhulst

Gavin Hayman at the Huffington Post: “This week the British Prime Minister David Cameron is hosting an international anti-corruption summit. The scourge of anonymous shell companies and hidden identities rightly seizes the public’s imagination. We can all picture the suitcases of cash and tropical islands involved. As well as acting on offshore and onshore money laundering havens, world leaders at the summit should also be asking themselves where all this money is being stolen from in the first place.

The answer is mostly from public contracting: government spending through private companies to deliver works, goods and services to citizens. It is technical, dull and universally obscure. But it is the single biggest item of spending by government – amounting to a staggering $9,500,000,000,000 each year. This concentration of money, government discretion, and secrecy makes public contracting so vulnerable to corruption. Data on prosecutions tracked by the OECD Anti-Bribery Convention shows that roughly 60% of bribes were paid to win public contracts.

Corruption in contracting deprives ordinary people of vital goods and services, and sometimes even kills: I was one of many Londoners moved by Ai Wei Wei’s installation that memorialised the names of thousands of children killed in China’s Sichuan earthquake in 2008. Their supposed earthquake-proof schools collapsed on them like tofu.

Beyond corruption, inefficiency and mismanagement of public contracts cost countries billions. Governments just don’t seem to know what they are buying, when, from whom, and whether they got a good price.

This problem can be fixed. But it will require a set of innovations best described as open contracting: using accessible open data and better engagement so that citizens, government and business can follow the money in government contracts from planning to tendering to performance and closure. The coordination required can be hard work but it is achievable: any country can make substantial progress on open contracting with some political leadership. My organisation supports an open data standard and a free global helpdesk to assist governments, civil society, and business in this transition….(More)”

Accountable Algorithms

Curated on April 20, 2016August 3, 2018 by Stefaan Verhulst

Paper by Joshua A. Kroll et al: “Many important decisions historically made by people are now made by computers. Algorithms count votes, approve loan and credit card applications, target citizens or neighborhoods for police scrutiny, select taxpayers for an IRS audit, and grant or deny immigration visas.

The accountability mechanisms and legal standards that govern such decision processes have not kept pace with technology. The tools currently available to policymakers, legislators, and courts were developed to oversee human decision-makers and often fail when applied to computers instead: for example, how do you judge the intent of a piece of software? Additional approaches are needed to make automated decision systems — with their potentially incorrect, unjustified or unfair results — accountable and governable. This Article reveals a new technological toolkit to verify that automated decisions comply with key standards of legal fairness.

We challenge the dominant position in the legal literature that transparency will solve these problems. Disclosure of source code is often neither necessary (because of alternative techniques from computer science) nor sufficient (because of the complexity of code) to demonstrate the fairness of a process. Furthermore, transparency may be undesirable, such as when it permits tax cheats or terrorists to game the systems determining audits or security screening.

The central issue is how to assure the interests of citizens, and society as a whole, in making these processes more accountable. This Article argues that technology is creating new opportunities — more subtle and flexible than total transparency — to design decision-making algorithms so that they better align with legal and policy objectives. Doing so will improve not only the current governance of algorithms, but also — in certain cases — the governance of decision-making in general. The implicit (or explicit) biases of human decision-makers can be difficult to find and root out, but we can peer into the “brain” of an algorithm: computational processes and purpose specifications can be declared prior to use and verified afterwards.

The technological tools introduced in this Article apply widely. They can be used in designing decision-making processes from both the private and public sectors, and they can be tailored to verify different characteristics as desired by decision-makers, regulators, or the public. By forcing a more careful consideration of the effects of decision rules, they also engender policy discussions and closer looks at legal standards. As such, these tools have far-reaching implications throughout law and society.

Part I of this Article provides an accessible and concise introduction to foundational computer science concepts that can be used to verify and demonstrate compliance with key standards of legal fairness for automated decisions without revealing key attributes of the decision or the process by which the decision was reached. Part II then describes how these techniques can assure that decisions are made with the key governance attribute of procedural regularity, meaning that decisions are made under an announced set of rules consistently applied in each case. We demonstrate how this approach could be used to redesign and resolve issues with the State Department’s diversity visa lottery. In Part III, we go further and explore how other computational techniques can assure that automated decisions preserve fidelity to substantive legal and policy choices. We show how these tools may be used to assure that certain kinds of unjust discrimination are avoided and that automated decision processes behave in ways that comport with the social or legal standards that govern the decision. We also show how algorithmic decision-making may even complicate existing doctrines of disparate treatment and disparate impact, and we discuss some recent computer science work on detecting and removing discrimination in algorithms, especially in the context of big data and machine learning. And lastly in Part IV, we propose an agenda to further synergistic collaboration between computer science, law and policy to advance the design of automated decision processes for accountability….(More)”

Fairness in Machine Learning

Curated on April 13, 2016August 3, 2018 by Stefaan Verhulst

Presentation by Delip Rao: “…The models you create have power to get people arrested or vindicated, get loans approved or rejected, determine what interest rate should be charged for such loans, who should be shown to you in your long list of pursuits on your Tinder, what news do you read, who gets called for a job phone screen or even a college admission… the list goes on.

So what can you do about it?…

I have detailed notes for some of these slides. If you would like to follow those, try going directly to Google Slides.

Innovation and Its Enemies: Why People Resist New Technologies

Curated on April 11, 2016November 14, 2018 by Stefaan Verhulst

]Book by Calestous Juma: “The rise of artificial intelligence has rekindled a long-standing debate regarding the impact of technology on employment. This is just one of many areas where exponential advances in technology signal both hope and fear, leading to public controversy. This book shows that many debates over new technologies are framed in the context of risks to moral values, human health, and environmental safety. But it argues that behind these legitimate concerns often lie deeper, but unacknowledged, socioeconomic considerations. Technological tensions are often heightened by perceptions that the benefits of new technologies will accrue only to small sections of society while the risks will be more widely distributed. Similarly, innovations that threaten to alter cultural identities tend to generate intense social concern. As such, societies that exhibit great economic and political inequities are likely to experience heightened technological controversies.

Drawing from nearly 600 years of technology history, Innovation and Its Enemies identifies the tension between the need for innovation and the pressure to maintain continuity, social order, and stability as one of today’s biggest policy challenges. It reveals the extent to which modern technological controversies grow out of distrust in public and private institutions. Using detailed case studies of coffee, the printing press, margarine, farm mechanization, electricity, mechanical refrigeration, recorded music, transgenic crops, and transgenic animals, it shows how new technologies emerge, take root, and create new institutional ecologies that favor their establishment in the marketplace. The book uses these lessons from history to contextualize contemporary debates surrounding technologies such as artificial intelligence, online learning, 3D printing, gene editing, robotics, drones, and renewable energy. It ultimately makes the case for shifting greater responsibility to public leaders to work with scientists, engineers, and entrepreneurs to manage technological change, make associated institutional adjustments, and expand public engagement on scientific and technological matters….(More)”

Accountable machines: bureaucratic cybernetics?

Curated on March 28, 2016November 14, 2018 by Stefaan Verhulst

Alison Powell at LSE Media Policy Project Blog: “Algorithms are everywhere, or so we are told, and the black boxes of algorithmic decision-making make oversight of processes that regulators and activists argue ought to be transparent more difficult than in the past. But when, and where, and which machines do we wish to make accountable, and for what purpose? In this post I discuss how algorithms discussed by scholars are most commonly those at work on media platforms whose main products are the social networks and attention of individuals. Algorithms, in this case, construct individual identities through patterns of behaviour, and provide the opportunity for finely targeted products and services. While there are serious concerns about, for instance, price discrimination, algorithmic systems for communicating and consuming are, in my view, less inherently problematic than processes that impact on our collective participation and belonging as citizenship. In this second sphere, algorithmic processes – especially machine learning – combine with processes of governance that focus on individual identity performance to profoundly transform how citizenship is understood and undertaken.

Communicating and consuming

In the communications sphere, algorithms are what makes it possible to make money from the web for example through advertising brokerage platforms that help companies bid for ads on major newspaper websites. IP address monitoring, which tracks clicks and web activity, creates detailed consumer profiles and transform the everyday experience of communication into a constantly-updated production of consumer information. This process of personal profiling is at the heart of many of the concerns about algorithmic accountability. The consequence of perpetual production of data by individuals and the increasing capacity to analyse it even when it doesn’t appear to relate has certainly revolutionalised advertising by allowing more precise targeting, but what has it done for areas of public interest?

John Cheney-Lippold identifies how the categories of identity are now developed algorithmically, since a category like gender is not based on self-discloure, but instead on patterns of behaviour that fit with expectations set by previous alignment to a norm. In assessing ‘algorithmic identities’, he notes that these produce identity profiles which are narrower and more behaviour-based than the identities that we perform. This is a result of the fact that many of the systems that inspired the design of algorithmic systems were based on using behaviour and other markers to optimise consumption. Algorithmic identity construction has spread from the world of marketing to the broader world of citizenship – as evidenced by the Citizen Ex experiment shown at the Web We Want Festival in 2015.

Individual consumer-citizens

What’s really at stake is that the expansion of algorithmic assessment of commercially derived big data has extended the frame of the individual consumer into all kinds of other areas of experience. In a supposed ‘age of austerity’ when governments believe it’s important to cut costs, this connects with the view of citizens as primarily consumers of services, and furthermore, with the idea that a citizen is an individual subject whose relation to a state can be disintermediated given enough technology. So, with sensors on your garbage bins you don’t need to even remember to take them out. With pothole reporting platforms like FixMyStreet, a city government can be responsive to an aggregate of individual reports. But what aspects of our citizenship are collective? When, in the algorithmic state, can we expect to be together?

Put another way, is there any algorithmic process to value the long term education, inclusion, and sustenance of a whole community for example through library services?…

Seeing algorithms – machine learning in particular – as supporting decision-making for broad collective benefit rather than as part of ever more specific individual targeting and segmentation might make them more accountable. But more importantly, this would help algorithms support society – not just individual consumers….(More)”

It’s not big data that discriminates – it’s the people that use it

Curated on March 22, 2016August 15, 2018 by Stefaan Verhulst

Reuben Binns in the Conversation: “Data can’t be racist or sexist, but the way it is used can help reinforce discrimination. The internet means more data is collected about us than ever before and it is used to make automatic decisions that can hugely affect our lives, from our credit scores to our employment opportunities.

If that data reflects unfair social biases against sensitive attributes, such as our race or gender, the conclusions drawn from that data might also be based on those biases.

But this era of “big data” doesn’t need to to entrench inequality in this way. If we build smarter algorithms to analyse our information and ensure we’re aware of how discrimination and injustice may be at work, we can actually use big data to counter our human prejudices.

This kind of problem can arise when computer models are used to make predictions in areas such as insurance, financial loans and policing. If members of a certain racial group have historically been more likely to default on their loans, or been more likely to be convicted of a crime, then the model can deem these people more risky. That doesn’t necessarily mean that these people actually engage in more criminal behaviour or are worse at managing their money. They may just be disproportionately targeted by police and sub-prime mortgage salesmen.

Excluding sensitive attributes

Data scientist Cathy O’Neil has written about her experience of developing models for homeless services in New York City. The models were used to predict how long homeless clients would be in the system and to match them with appropriate services. She argues that including race in the analysis would have been unethical.

If the data showed white clients were more likely to find a job than black ones, the argument goes, then staff might focus their limited resources on those white clients that would more likely have a positive outcome. While sociological research has unveiled the ways that racial disparities in homelessness and unemployment are the result of unjust discrimination, algorithms can’t tell the difference between just and unjust patterns. And so datasets should exclude characteristics that may be used to reinforce the bias, such as race.

But this simple response isn’t necessarily the answer. For one thing, machine learning algorithms can often infer sensitive attributes from a combination of other, non-sensitive facts. People of a particular race may be more likely to live in a certain area, for example. So excluding those attributes may not be enough to remove the bias….

An enlightened service provider might, upon seeing the results of the analysis, investigate whether and how racism is a barrier to their black clients getting hired. Equipped with this knowledge they could begin to do something about it. For instance, they could ensure that local employers’ hiring practices are fair and provide additional help to those applicants more likely to face discrimination. The moral responsibility lies with those responsible for interpreting and acting on the model, not the model itself.

So the argument that sensitive attributes should be stripped from the datasets we use to train predictive models is too simple. Of course, collecting sensitive data should be carefully regulated because it can easily be misused. But misuse is not inevitable, and in some cases, collecting sensitive attributes could prove absolutely essential in uncovering, predicting, and correcting unjust discrimination. For example, in the case of homeless services discussed above, the city would need to collect data on ethnicity in order to discover potential biases in employment practices….(More)

A machine intelligence commission for the UK

Curated on March 1, 2016August 3, 2018 by Stefaan Verhulst

Geoff Mulgan at NESTA: ” This paper makes the case for creating a Machine Intelligence Commission – a new public institution to help the development of new generations of algorithms, machine learning tools and uses of big data, ensuring that the public interest is protected.

I argue that new institutions of this kind – which can interrogate, inspect and influence technological development – are a precondition for growing informed public trust. That trust will, in turn, be essential if we are to reap the full potential public and economic benefits from new technologies. The proposal draws on lessons from fields such as human fertilisation, biotech and energy, which have shown how trust can be earned, and how new industries can be grown. It also draws on lessons from the mistakes made in fields like GM crops and personal health data, where lack of trust has impeded progress….(More)”

Drones better than human rescuers at following mountain pathways

Curated on February 23, 2016July 19, 2019 by Stefaan Verhulst

Springwise: “Every year in Switzerland, emergency centers respond to around 1,000 call outs for lost and injured hikers. It can often take hours and significant manpower to locate lost mountaineers, but new software for quadcopter drones is making the hunt quicker and easier, and has the potential to help find human survivors in disaster zones around the world.

The drone uses a computer algorithm called a Deep Neural Network. The program was developed by researchers at the University of Zurich and the Dalle Molle Institute for Artificial Intelligence. The drone uses the algorithm to learn trails and paths through a pair of small cameras, interpreting the images and recognizing man-made pathways. Even when working on a previously unseen trail, it was able to guess the correct direction in 85 percent of the cases. The drones’ speed and accuracy make them more effective than human trackers.

Drohnen-Scaramuzza-2
The researchers hope that eventually multiple small drones could be combined with human search and rescue missions, to cover more terrain and find people faster. The drones can cover terrain quickly and check hazardous areas to minimize risk to human workers, and its AI can identify paths and avoid crashing without any human involvement….(More)”

Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions

Curated on February 22, 2016August 3, 2018 by Stefaan Verhulst

Richard A. Berk, Susan B. Sorenson and Geoffrey Barnes in the The Journal of Empirical Legal Studies: “Arguably the most important decision at an arraignment is whether to release an offender until the date of his or her next scheduled court appearance. Under the Bail Reform Act of 1984, threats to public safety can be a key factor in that decision. Implicitly, a forecast of “future dangerousness” is required. In this article, we consider in particular whether usefully accurate forecasts of domestic violence can be obtained. We apply machine learning to data on over 28,000 arraignment cases from a major metropolitan area in which an offender faces domestic violence charges. One of three possible post-arraignment outcomes is forecasted within two years: (1) a domestic violence arrest associated with a physical injury, (2) a domestic violence arrest not associated with a physical injury, and (3) no arrests for domestic violence. We incorporate asymmetric costs for different kinds of forecasting errors so that very strong statistical evidence is required before an offender is forecasted to be a good risk. When an out-of-sample forecast of no post-arraignment domestic violence arrests within two years is made, it is correct about 90 percent of the time. Under current practice within the jurisdiction studied, approximately 20 percent of those released after an arraignment for domestic violence are arrested within two years for a new domestic violence offense. If magistrates used the methods we have developed and released only offenders forecasted not to be arrested for domestic violence within two years after an arraignment, as few as 10 percent might be arrested. The failure rate could be cut nearly in half. Over a typical 24-month period in the jurisdiction studied, well over 2,000 post-arraignment arrests for domestic violence perhaps could be averted….(More)”