Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again


Book by Eric Topol: “Medicine has become inhuman, to disastrous effect. The doctor-patient relationship–the heart of medicine–is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved….(More)”.

Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems


Introduction by A.F. Winfield, K. Michael, J. Pitt, V. Evers of Special Issue of Proceedings of the IEEE: “…The primary focus of this special issue is machine ethics, that is the question of how autonomous systems can be imbued with ethical values. Ethical autonomous systems are needed because, inevitably, near future systems are moral agents; consider driverless cars, or medical diagnosis AIs, both of which will need to make choices with ethical consequences. This special issue includes papers that describe both implicit ethical agents, that is machines designed to avoid unethical outcomes, and explicit ethical agents: machines which either encode or learn ethics and determine actions based on those ethics. Of course ethical machines are socio-technical systems thus, as a secondary focus, this issue includes papers that explore the societal and regulatory implications of machine ethics, including the question of ethical governance. Ethical governance is needed in order to develop standards and processes that allow us to transparently and robustly assure the safety of ethical autonomous systems and hence build public trust and confidence….(More)?

Regulating disinformation with artificial intelligence


Paper for the European Parliamentary Research Service: “This study examines the consequences of the increasingly prevalent use of artificial intelligence (AI) disinformation initiatives upon freedom of expression, pluralism and the functioning of a democratic polity. The study examines the trade-offs in using automated technology to limit the spread of disinformation online. It presents options (from self-regulatory to legislative) to regulate automated content recognition (ACR) technologies in this context. Special attention is paid to the opportunities for the European Union as a whole to take the lead in setting the framework for designing these technologies in a way that enhances accountability and transparency and respects free speech. The present project reviews some of the key academic and policy ideas on technology and disinformation and highlights their relevance to European policy.

Chapter 1 introduces the background to the study and presents the definitions used. Chapter 2 scopes the policy boundaries of disinformation from economic, societal and technological perspectives, focusing on the media context, behavioural economics and technological regulation. Chapter 3 maps and evaluates existing regulatory and technological responses to disinformation. In Chapter 4, policy options are presented, paying particular attention to interactions between technological solutions, freedom of expression and media pluralism….(More)”.

Is Ethical A.I. Even Possible?


Cade Metz at The New York Times: ” When a news article revealed that Clarifaiwas working with the Pentagon and some employees questioned the ethics of building artificial intelligence that analyzed video captured by drones, the company said the project would save the lives of civilians and soldiers.

“Clarifai’s mission is to accelerate the progress of humanity with continually improving A.I.,” read a blog post from Matt Zeiler, the company’s founder and chief executive, and a prominent A.I. researcher. Later, in a news media interview, Mr. Zeiler announced a new management position that would ensure all company projects were ethically sound.

As activists, researchers, and journalists voice concerns over the rise of artificial intelligence, warning against biased, deceptive and malicious applications, the companies building this technology are responding. From tech giants like Google and Microsoft to scrappy A.I. start-ups, many are creating corporate principles meant to ensure their systems are designed and deployed in an ethical way. Some set up ethics officers or review boards to oversee these principles.

But tensions continue to rise as some question whether these promises will ultimately be kept. Companies can change course. Idealism can bow to financial pressure. Some activists — and even some companies — are beginning to argue that the only way to ensure ethical practices is through government regulation....

As companies and governments deploy these A.I. technologies, researchers are also realizing that some systems are woefully biased. Facial recognition services, for instance, can be significantly less accurate when trying to identify women or someone with darker skin. Other systems may include security holes unlike any seen in the past. Researchers have shown that driverless cars can be fooled into seeing things that are not really there.

All this means that building ethical artificial intelligence is an enormously complex task. It gets even harder when stakeholders realize that ethics are in the eye of the beholder.

As some Microsoft employees protest the company’s military contracts, Mr. Smith said that American tech companies had long supported the military and that they must continue to do so. “The U.S. military is charged with protecting the freedoms of this country,” he told the conference. “We have to stand by the people who are risking their lives.”

Though some Clarifai employees draw an ethical line at autonomous weapons, others do not. Mr. Zeiler argued that autonomous weapons will ultimately save lives because they would be more accurate than weapons controlled by human operators. “A.I. is an essential tool in helping weapons become more accurate, reducing collateral damage, minimizing civilian casualties and friendly fire incidents,” he said in a statement.

Google worked on the same Pentagon project as Clarifai, and after a protest from company employees, the tech giant ultimately ended its involvement. But like Clarifai, as many as 20 other companies have worked on the project without bowing to ethical concerns.

After the controversy over its Pentagon work, Google laid down a set of “A.I. principles” meant as a guide for future projects. But even with the corporate rules in place, some employees left the company in protest. The new principles are open to interpretation. And they are overseen by executives who must also protect the company’s financial interests….

In their open letter, the Clarifai employees said they were unsure whether regulation was the answer to the many ethical questions swirling around A.I. technology, arguing that the immediate responsibility rested with the company itself….(More)”.

Algorithmic fairness: A code-based primer for public-sector data scientists


Paper by Ken Steif and Sydney Goldstein: “As the number of government algorithms grow, so does the need to evaluate algorithmic fairness. This paper has three goals. First, we ground the notion of algorithmic fairness in the context of disparate impact, arguing that for an algorithm to be fair, its predictions must generalize across different protected groups. Next, two algorithmic use cases are presented with code examples for how to evaluate fairness. Finally, we promote the concept of an open source repository of government algorithmic “scorecards,” allowing stakeholders to compare across algorithms and use cases….(More)”.

Governance of artificial intelligence and personal health information


Jenifer Sunrise Winter in Digital Policy, Regulation and Governance: “This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions….

This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.

Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security….(More).

Claudette: an automated detector of potentially unfair clauses in online terms of service


Marco Lippi et al in AI and the Law Journal: “Terms of service of on-line platforms too often contain clauses that are potentially unfair to the consumer. We present an experimental study where machine learning is employed to automatically detect such potentially unfair clauses. Results show that the proposed system could provide a valuable tool for lawyers and consumers alike….(More)”.

Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice


Paper by Rashida Richardson, Jason Schultz, and Kate Crawford: “Law enforcement agencies are increasingly using algorithmic predictive policing systems to forecast criminal activity and allocate police resources. Yet in numerous jurisdictions, these systems are built on data produced within the context of flawed, racially fraught and sometimes unlawful practices (‘dirty policing’). This can include systemic data manipulation, falsifying police reports, unlawful use of force, planted evidence, and unconstitutional searches. These policing practices shape the environment and the methodology by which data is created, which leads to inaccuracies, skews, and forms of systemic bias embedded in the data (‘dirty data’). Predictive policing systems informed by such data cannot escape the legacy of unlawful or biased policing practices that they are built on. Nor do claims by predictive policing vendors that these systems provide greater objectivity, transparency, or accountability hold up. While some systems offer the ability to see the algorithms used and even occasionally access to the data itself, there is no evidence to suggest that vendors independently or adequately assess the impact that unlawful and bias policing practices have on their systems, or otherwise assess how broader societal biases may affect their systems.

In our research, we examine the implications of using dirty data with predictive policing, and look at jurisdictions that (1) have utilized predictive policing systems and (2) have done so while under government commission investigations or federal court monitored settlements, consent decrees, or memoranda of agreement stemming from corrupt, racially biased, or otherwise illegal policing practices. In particular, we examine the link between unlawful and biased police practices and the data used to train or implement these systems across thirteen case studies. We highlight three of these: (1) Chicago, an example of where dirty data was ingested directly into the city’s predictive system; (2) New Orleans, an example where the extensive evidence of dirty policing practices suggests an extremely high risk that dirty data was or will be used in any predictive policing application, and (3) Maricopa County where despite extensive evidence of dirty policing practices, lack of transparency and public accountability surrounding predictive policing inhibits the public from assessing the risks of dirty data within such systems. The implications of these findings have widespread ramifications for predictive policing writ large. Deploying predictive policing systems in jurisdictions with extensive histories of unlawful police practices presents elevated risks that dirty data will lead to flawed, biased, and unlawful predictions which in turn risk perpetuating additional harm via feedback loops throughout the criminal justice system. Thus, for any jurisdiction where police have been found to engage in such practices, the use of predictive policing in any context must be treated with skepticism and mechanisms for the public to examine and reject such systems are imperative….(More)”.

7 things we’ve learned about computer algorithms


Aaron Smith at Pew Research Center: “Algorithms are all around us, using massive stores of data and complex analytics to make decisions with often significant impacts on humans – from choosing the content people see on social media to judging whether a person is a good credit risk or job candidate. Pew Research Center released several reports in 2018 that explored the role and meaning of algorithms in people’s lives today. Here are some of the key themes that emerged from that research.

  1. Algorithmically generated content platforms play a prominent role in Americans’ information diets. Sizable shares of U.S. adults now get news on sites like Facebook or YouTube that use algorithms to curate the content they show to their users. A study by the Center found that 81% of YouTube users say they at least occasionally watch the videos suggested by the platform’s recommendation algorithm, and that these recommendations encourage users to watch progressively longer content as they click through the videos suggested by the site.
  2. The inner workings of even the most common algorithms can be confusing to users. Facebook is among the most popular social media platforms, but roughly half of Facebook users – including six-in-ten users ages 50 and older – say they do not understand how the site’s algorithmically generated news feed selects which posts to show them. And around three-quarters of Facebook users are not aware that the site automatically estimates their interests and preferences based on their online behaviors in order to deliver them targeted advertisements and other content.
  3. The public is wary of computer algorithms being used to make decisions with real-world consequences. The public expresses widespread concern about companies and other institutions using computer algorithms in situations with potential impacts on people’s lives. More than half (56%) of U.S. adults think it is unacceptable to use automated criminal risk scores when evaluating people who are up for parole. And 68% think it is unacceptable for companies to collect large quantities of data about individuals for the purposes of offering them deals or other financial incentives. When asked to elaborate about their worries, many feel that these programs violate people’s privacy, are unfair, or simply will not work as well as decisions made by humans….(More)”.

Decoding Algorithms


Malcalester University: “Ada Lovelace probably didn’t foresee the impact of the mathematical formula she published in 1843, now considered the first computer algorithm.

Nor could she have anticipated today’s widespread use of algorithms, in applications as different as the 2016 U.S. presidential campaign and Mac’s first-year seminar registration. “Over the last decade algorithms have become embedded in every aspect of our lives,” says Shilad Sen, professor in Macalester’s Math, Statistics, and Computer Science (MSCS) Department.

How do algorithms shape our society? Why is it important to be aware of them? And for readers who don’t know, what is an algorithm, anyway?…(More)”.