How Humans Judge Machines

Curated on Posted on by Stefaan Verhulst

Open Access Book by César A. Hidalgo et al : “How would you feel about losing your job to a machine? How about a tsunami alert system that fails? Would you react differently to acts of discrimination depending on whether they were carried out by a machine or by a human? What about public surveillance? How Humans Judge Machines compares people’s reactions to actions performed by humans and machines. Using data collected in dozens of experiments, this book reveals the biases that permeate human-machine interactions. Are there conditions in which we judge machines unfairly?

Is our judgment of machines affected by the moral dimensions of a scenario? Is our judgment of machine correlated with demographic factors such as education or gender? César Hidalgo and colleagues use hard science to take on these pressing technological questions. Using randomized experiments, they create revealing counterfactuals and build statistical models to explain how people judge artificial intelligence and whether they do it fairly. Through original research, How Humans Judge Machines bring us one step closer to understanding the ethical consequences of AI…(More)”.

How One State Managed to Actually Write Rules on Facial Recognition

Curated on Posted on by Stefaan Verhulst

Kashmir Hill at The New York Times: “Though police have been using facial recognition technology for the last two decades to try to identify unknown people in their investigations, the practice of putting the majority of Americans into a perpetual photo lineup has gotten surprisingly little attention from lawmakers and regulators. Until now.

Lawmakers, civil liberties advocates and police chiefs have debated whether and how to use the technology because of concerns about both privacy and accuracy. But figuring out how to regulate it is tricky. So far, that has meant an all-or-nothing approach. City Councils in Oakland, Portland, San Francisco, Minneapolis and elsewhere have banned police use of the technology, largely because of bias in how it works. Studies in recent years by MIT researchers and the federal government found that many facial recognition algorithms are most accurate for white men, but less so for everyone else.

At the same time, automated facial recognition has become a powerful investigative tool, helping to identify child molesters and, in a recent high-profile example, people who participated in the Jan. 6 riot at the Capitol. Law enforcement officials in Vermont want the state’s ban lifted because there “could be hundreds of kids waiting to be saved.”

That’s why a new law in Massachusetts is so interesting: It’s not all or nothing. The state managed to strike a balance on regulating the technology, allowing law enforcement to harness the benefits of the tool, while building in protections that might prevent the false arrests that have happened before….(More)”.

Artificial Intelligence as an Anti-Corruption Tool (AI-ACT)

Curated on Posted on by Stefaan Verhulst

Paper by Nils Köbis, Christopher Starke, and Iyad Rahwan: “Corruption continues to be one of the biggest societal challenges of our time. New hope is placed in Artificial Intelligence (AI) to serve as an unbiased anti-corruption agent. Ever more available (open) government data paired with unprecedented performance of such algorithms render AI the next frontier in anti-corruption. Summarizing existing efforts to use AI-based anti-corruption tools (AI-ACT), we introduce a conceptual framework to advance research and policy. It outlines why AI presents a unique tool for top-down and bottom-up anti-corruption approaches. For both approaches, we outline in detail how AI-ACT present different potentials and pitfalls for (a) input data, (b) algorithmic design, and (c) institutional implementation. Finally, we venture a look into the future and flesh out key questions that need to be addressed to develop AI-ACT while considering citizens’ views, hence putting “society in the loop”….(More)”.

A.I. Here, There, Everywhere

Curated on Posted on by Stefaan Verhulst

Craig S. Smith at the New York Times: “I wake up in the middle of the night. It’s cold.

“Hey, Google, what’s the temperature in Zone 2,” I say into the darkness. A disembodied voice responds: “The temperature in Zone 2 is 52 degrees.” “Set the heat to 68,” I say, and then I ask the gods of artificial intelligence to turn on the light.

Many of us already live with A.I., an array of unseen algorithms that control our Internet-connected devices, from smartphones to security cameras and cars that heat the seats before you’ve even stepped out of the house on a frigid morning.

But, while we’ve seen the A.I. sun, we have yet to see it truly shine.

Researchers liken the current state of the technology to cellphones of the 1990s: useful, but crude and cumbersome. They are working on distilling the largest, most powerful machine-learning models into lightweight software that can run on “the edge,” meaning small devices such as kitchen appliances or wearables. Our lives will gradually be interwoven with brilliant threads of A.I.

Our interactions with the technology will become increasingly personalized. Chatbots, for example, can be clumsy and frustrating today, but they will eventually become truly conversational, learning our habits and personalities and even develop personalities of their own. But don’t worry, the fever dreams of superintelligent machines taking over, like HAL in “2001: A Space Odyssey,” will remain science fiction for a long time to come; consciousness, self-awareness and free will in machines are far beyond the capabilities of science today.

Privacy remains an issue, because artificial intelligence requires data to learn patterns and make decisions. But researchers are developing methods to use our data without actually seeing it — so-called federated learning, for example — or encrypt it in ways that currently can’t be hacked….(More)”

Regulation of Algorithmic Tools in the United States

Curated on Posted on by Stefaan Verhulst

Paper by Christopher S. Yoo and Alicia Lai: “Policymakers in the United States have just begun to address regulation of artificial intelligence technologies in recent years, gaining momentum through calls for additional research funding, piece-meal guidance, proposals, and legislation at all levels of government. This Article provides an overview of high-level federal initiatives for general artificial intelligence (AI) applications set forth by the U.S. president and responding agencies, early indications from the incoming Biden Administration, targeted federal initiatives for sector-specific AI applications, pending federal legislative proposals, and state and local initiatives. The regulation of the algorithmic ecosystem will continue to evolve as the United States continues to search for the right balance between ensuring public safety and transparency and promoting innovation and competitiveness on the global stage….(More)”.

A definition, benchmark and database of AI for social good initiatives

Curated on Posted on by Stefaan Verhulst

Paper by Josh Cowls, Andreas Tsamados, Mariarosaria Taddeo & Luciano Floridi: “Initiatives relying on artificial intelligence (AI) to deliver socially beneficial outcomes—AI for social good (AI4SG)—are on the rise. However, existing attempts to understand and foster AI4SG initiatives have so far been limited by the lack of normative analyses and a shortage of empirical evidence. In this Perspective, we address these limitations by providing a definition of AI4SG and by advocating the use of the United Nations’ Sustainable Development Goals (SDGs) as a benchmark for tracing the scope and spread of AI4SG. We introduce a database of AI4SG projects gathered using this benchmark, and discuss several key insights, including the extent to which different SDGs are being addressed. This analysis makes possible the identification of pressing problems that, if left unaddressed, risk hampering the effectiveness of AI4SG initiatives….(More)”.

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Book by Kate Crawford: “What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality. Drawing on more than a decade of research, award-winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind “automated” services, to the data AI collects from us. 

Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world…(More)”.

AI Ethics Needs Good Data

Curated on Posted on by Stefaan Verhulst

Paper by Angela Daly, S Kate Devitt, and Monique Mann: “In this chapter we argue that discourses on AI must transcend the language of ‘ethics’ and engage with power and political economy in order to constitute ‘Good Data’. In particular, we must move beyond the depoliticised language of ‘ethics’ currently deployed (Wagner 2018) in determining whether AI is ‘good’ given the limitations of ethics as a frame through which AI issues can be viewed. In order to circumvent these limits, we use instead the language and conceptualisation of ‘Good Data’, as a more expansive term to elucidate the values, rights and interests at stake when it comes to AI’s development and deployment, as well as that of other digital technologies.

Good Data considerations move beyond recurring themes of data protection/privacy and the FAT (fairness, transparency and accountability) movement to include explicit political economy critiques of power. Instead of yet more ethics principles (that tend to say the same or similar things anyway), we offer four ‘pillars’ on which Good Data AI can be built: community, rights, usability and politics. Overall we view AI’s ‘goodness’ as an explicly political (economy) question of power and one which is always related to the degree which AI is created and used to increase the wellbeing of society and especially to increase the power of the most marginalized and disenfranchised. We offer recommendations and remedies towards implementing ‘better’ approaches towards AI. Our strategies enable a different (but complementary) kind of evaluation of AI as part of the broader socio-technical systems in which AI is built and deployed….(More)”.

Towards Algorithm Auditing: A Survey on Managing Legal, Ethical and Technological Risks of AI, ML and Associated Algorithms

Curated on Posted on by Stefaan Verhulst

Paper by Adriano Koshiyama: “Business reliance on algorithms are becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include VW’s Dieselgate scandal with fines worth of $34.69B, Knight Capital’s bankruptcy (~$450M) by a glitch in its algorithmic trading system, and Amazon’s AI recruiting tool being scrapped after showing bias against women. In response, governments are legislating and imposing bans, regulators fining companies, and the Judiciary discussing potentially making algorithms artificial “persons” in Law.

Soon there will be ‘billions’ of algorithms making decisions with minimal human intervention; from autonomous vehicles and finance, to medical treatment, employment, and legal decisions. Indeed, scaling to problems beyond the human is a major point of using such algorithms in the first place. As with Financial Audit, governments, business and society will require Algorithm Audit; formal assurance that algorithms are legal, ethical and safe. A new industry is envisaged: Auditing and Assurance of Algorithms (cf. Data privacy), with the remit to professionalize and industrialize AI, ML and associated algorithms.

The stakeholders range from those working on policy and regulation, to industry practitioners and developers. We also anticipate the nature and scope of the auditing levels and framework presented will inform those interested in systems of governance and compliance to regulation/standards. Our goal in this paper is to survey the key areas necessary to perform auditing and assurance, and instigate the debate in this novel area of research and practice….(More)”.

Practical Fairness

Curated on Posted on by Stefaan Verhulst

Book by Aileen Nielsen: “Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we’re trying to fight in the real world. But what does fairness mean when it comes to code? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that’s fair and free of bias.

Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms.

  • Identify potential bias and discrimination in data science models
  • Use preventive measures to minimize bias when developing data modeling pipelines
  • Understand what data pipeline components implicate security and privacy concerns
  • Write data processing and modeling code that implements best practices for fairness
  • Recognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning models
  • Apply normative and legal concepts relevant to evaluating the fairness of machine learning models…(More)”.