Artificial Intelligence: Risks to Privacy and Democracy

Curated on Posted on by Stefaan Verhulst

Karl Manheim and Lyric Kaplan at Yale Journal of Law and Technology: “A “Democracy Index” is published annually by the Economist. For 2017, it reported that half of the world’s countries scored lower than the previous year. This included the United States, which was demoted from “full democracy” to “flawed democracy.” The principal factor was “erosion of confidence in government and public institutions.” Interference by Russia and voter manipulation by Cambridge Analytica in the 2016 presidential election played a large part in that public disaffection.

Threats of these kinds will continue, fueled by growing deployment of artificial intelligence (AI) tools to manipulate the preconditions and levers of democracy. Equally destructive is AI’s threat to decisional andinforma-tional privacy. AI is the engine behind Big Data Analytics and the Internet of Things. While conferring some consumer benefit, their principal function at present is to capture personal information, create detailed behavioral profiles and sell us goods and agendas. Privacy, anonymity and autonomy are the main casualties of AI’s ability to manipulate choices in economic and political decisions.

The way forward requires greater attention to these risks at the nation-al level, and attendant regulation. In its absence, technology giants, all of whom are heavily investing in and profiting from AI, will dominate not only the public discourse, but also the future of our core values and democratic institutions….(More)”.

Declaration of Cities Coalition for Digital Rights

Curated on Posted on by Stefaan Verhulst

New York City, Barcelona and Amsterdam: “We, the undersigned cities, formally come together to form the Cities Coalition for Digital Rights, to protect and uphold human rights on the internet at the local and global level.

The internet has become inseparable from our daily lives. Yet, every day, there are new cases of digital rights abuse, misuse and misinformation and concentration of power around the world: freedom of expression being censored; personal information, including our movements and communications, monitored, being shared and sold without consent; ‘black box’ algorithms being used to make unaccountable decisions; social media being used as a tool of harassment and hate speech; and democratic processes and public opinion being undermined.

As cities, the closest democratic institutions to the people, we are committed to eliminating impediments to harnessing technological opportunities that improve the lives of our constituents, and to providing trustworthy and secure digital services and infrastructures that support our communities. We strongly believe that human rights principles such as privacy, freedom of expression, and democracy must be incorporated by design into digital platforms starting with locally-controlled digital infrastructures and services.

As a coalition, and with the support of the United Nations Human Settlements Program (UN-Habitat), we will share best practices, learn from each other’s challenges and successes, and coordinate common initiatives and actions. Inspired by the Internet Rights and Principles Coalition (IRPC), the work of 300 international stakeholders over the past ten years, we are committed to the following five evolving principles:

01.Universal and equal access to the internet, and digital literacy

02.Privacy, data protection and security

03.Transparency, accountability, and non-discrimination of data, content and algorithms

04.Participatory Democracy, diversity and inclusion

05.Open and ethical digital service standards”

Another Use for A.I.: Finding Millions of Unregistered Voters

Curated on Posted on by Stefaan Verhulst

Steve Lohr at The New York Times: “The mechanics of elections that attract the most attention are casting and counting, snafus with voting machines and ballots and allegations of hacking and fraud. But Jeff Jonas, a prominent data scientist, is focused on something else: the integrity, updating and expansion of voter rolls.

“As I dove into the subject, it grew on me, the complexity and relevance of the problem,” he said.

As a result, Mr. Jonas has played a geeky, behind-the-scenes role in encouraging turnout for the midterm elections on Tuesday.

For the last four years, Mr. Jonas has used his software for a multistate project known as Electronic Registration Information Center that identifies eligible voters and cleans up voter rolls. Since its founding in 2012, the nonprofit center has identified 26 million people who are eligible but unregistered to vote, as well as 10 million registered voters who have moved, appear on more than one list or have died.

“I have no doubt that more people are voting as a result of ERIC,” said John Lindback, a former senior election administrator in Oregon and Alaska who was the center’s first executive director.

Voter rolls, like nearly every aspect of elections, are a politically charged issue. ERIC, brought together by the Pew Charitable Trusts, is meant to play it down the middle. It was started largely with professional election administrators, from both red and blue states.

But the election officials recognized that their headaches often boiled down to a data-handling challenge. Then Mr. Jonas added his technology, which has been developed and refined for decades. It is artificial intelligence software fine-tuned for spotting and resolving identities, whether people or things….(More)”.

A Behavioral Economics Approach to Digitalisation

Curated on Posted on by Stefaan Verhulst

Paper by Dirk Beerbaum and Julia M. Puaschunder: “A growing body of academic research in the field of behavioural economics, political science and psychology demonstrate how an invisible hand can nudge people’s decisions towards a preferred option. Contrary to the assumptions of the neoclassical economics, supporters of nudging argue that people have problems coping with a complex world, because of their limited knowledge and their restricted rationality. Technological improvement in the age of information has increased the possibilities to control the innocent social media users or penalise private investors and reap the benefits of their existence in hidden persuasion and discrimination. Nudging enables nudgers to plunder the simple uneducated and uninformed citizen and investor, who is neither aware of the nudging strategies nor able to oversee the tactics used by the nudgers (Puaschunder 2017a, b; 2018a, b).

The nudgers are thereby legally protected by democratically assigned positions they hold. The law of motion of the nudging societies holds an unequal concentration of power of those who have access to compiled data and coding rules, relevant for political power and influencing the investor’s decision usefulness (Puaschunder 2017a, b; 2018a, b). This paper takes as a case the “transparency technology XBRL (eXtensible Business Reporting Language)” (Sunstein 2013, 20), which should make data more accessible as well as usable for private investors. It is part of the choice architecture on regulation by governments (Sunstein 2013). However, XBRL is bounded to a taxonomy (Piechocki and Felden 2007).

Considering theoretical literature and field research, a representation issue (Beerbaum, Piechocki and Weber 2017) for principles-based accounting taxonomies exists, which intelligent machines applying Artificial Intelligence (AI) (Mwilu, Prat and Comyn-Wattiau 2015) nudge to facilitate decision usefulness. This paper conceptualizes ethical questions arising from the taxonomy engineering based on machine learning systems: Should the objective of the coding rule be to support or to influence human decision making or rational artificiality? This paper therefore advocates for a democratisation of information, education and transparency about nudges and coding rules (Puaschunder 2017a, b; 2018a, b)…(More)”.

The Inevitability of AI Law & Policy: Preparing Government for the Era of Autonomous Machines

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Public Knowledge: “Today, we’re happy to announce our newest white paper, “The Inevitability of AI Law & Policy: Preparing Government for the Era of Autonomous Machines,” by Public Knowledge General Counsel Ryan Clough. The paper argues that the rapid and pervasive rise of artificial intelligence risks exploiting the most marginalized and vulnerable in our society. To mitigate these harms, Clough advocates for a new federal authority to help the U.S. government implement fair and equitable AI. Such an authority should provide the rest of the government with the expertise and experience needed to achieve five goals crucial to building ethical AI systems:

  • Boosting sector-specific regulators and confronting overarching policy challenges raised by AI;
  • Protecting public values in government procurement and implementation of AI;
  • Attracting AI practitioners to civil service, and building durable and centralized AI expertise within government;
  • Identifying major gaps in the laws and regulatory frameworks that govern AI; and
  • Coordinating strategies and priorities for international AI governance.

“Any individual can be misjudged and mistreated by artificial intelligence,” Clough explains, “but the record to date indicates that it is significantly more likely to happen to the less powerful, who also have less recourse to do anything about it.” The paper argues that a new federal authority is the best way to meet the profound and novel challenges AI poses for us all….(More)”.

Algorithmic Government: Automating Public Services and Supporting Civil Servants in using Data Science Technologies

Curated on Posted on by Stefaan Verhulst

Zeynep Engin and Philip Treleaven in the Computer Journal:  “The data science technologies of artificial intelligence (AI), Internet of Things (IoT), big data and behavioral/predictive analytics, and blockchain are poised to revolutionize government and create a new generation of GovTech start-ups. The impact from the ‘smartification’ of public services and the national infrastructure will be much more significant in comparison to any other sector given government’s function and importance to every institution and individual.

Potential GovTech systems include Chatbots and intelligent assistants for public engagement, Robo-advisors to support civil servants, real-time management of the national infrastructure using IoT and blockchain, automated compliance/regulation, public records securely stored in blockchain distributed ledgers, online judicial and dispute resolution systems, and laws/statutes encoded as blockchain smart contracts. Government is potentially the major ‘client’ and also ‘public champion’ for these new data technologies. This review paper uses our simple taxonomy of government services to provide an overview of data science automation being deployed by governments world-wide. The goal of this review paper is to encourage the Computer Science community to engage with government to develop these new systems to transform public services and support the work of civil servants….(More)”.

Declaration on Ethics and Data Protection in Artifical Intelligence

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Declaration: “…The 40th International Conference of Data Protection and Privacy Commissioners considers that any creation, development and use of artificial intelligence systems shall fully respect human rights, particularly the rights to the protection of personal data and to privacy, as well as human dignity, non-discrimination and fundamental values, and shall provide solutions to allow individuals to maintain control and understanding of artificial intelligence systems.

The Conference therefore endorses the following guiding principles, as its core values to preserve human rights in the development of artificial intelligence:

  1. Artificial intelligence and machine learning technologies should be designed, developed and used in respect of fundamental human rights and in accordance with the fairness principle, in particular by:
  2. Considering individuals’ reasonable expectations by ensuring that the use of artificial intelligence systems remains consistent with their original purposes, and that the data are used in a way that is not incompatible with the original purpose of their collection,
  3. taking into consideration not only the impact that the use of artificial intelligence may have on the individual, but also the collective impact on groups and on society at large,
  4. ensuring that artificial intelligence systems are developed in a way that facilitates human development and does not obstruct or endanger it, thus recognizing the need for delineation and boundaries on certain uses,…(More)

When AI Misjudgment Is Not an Accident

Curated on Posted on by Stefaan Verhulst

Douglas Yeung at Scientific American: “The conversation about unconscious bias in artificial intelligence often focuses on algorithms that unintentionally cause disproportionate harm to entire swaths of society—those that wrongly predict black defendants will commit future crimes, for example, or facial-recognition technologies developed mainly by using photos of white men that do a poor job of identifying women and people with darker skin.

But the problem could run much deeper than that. Society should be on guard for another twist: the possibility that nefarious actors could seek to attack artificial intelligence systems by deliberately introducing bias into them, smuggled inside the data that helps those systems learn. This could introduce a worrisome new dimension to cyberattacks, disinformation campaigns or the proliferation of fake news.

According to a U.S. government study on big data and privacy, biased algorithms could make it easier to mask discriminatory lending, hiring or other unsavory business practices. Algorithms could be designed to take advantage of seemingly innocuous factors that can be discriminatory. Employing existing techniques, but with biased data or algorithms, could make it easier to hide nefarious intent. Commercial data brokers collect and hold onto all kinds of information, such as online browsing or shopping habits, that could be used in this way.

Biased data could also serve as bait. Corporations could release biased data with the hope competitors would use it to train artificial intelligence algorithms, causing competitors to diminish the quality of their own products and consumer confidence in them.

Algorithmic bias attacks could also be used to more easily advance ideological agendas. If hate groups or political advocacy organizations want to target or exclude people on the basis of race, gender, religion or other characteristics, biased algorithms could give them either the justification or more advanced means to directly do so. Biased data also could come into play in redistricting efforts that entrench racial segregation (“redlining”) or restrict voting rights.

Finally, national security threats from foreign actors could use deliberate bias attacks to destabilize societies by undermining government legitimacy or sharpening public polarization. This would fit naturally with tactics that reportedly seek to exploit ideological divides by creating social media posts and buying online ads designed to inflame racial tensions….(More)”.

This is how computers “predict the future”

Curated on Posted on by Stefaan Verhulst

Dan Kopf at Quartz: “The poetically named “random forest” is one of data science’s most-loved prediction algorithms. Developed primarily by statistician Leo Breiman in the 1990s, the random forest is cherished for its simplicity. Though it is not always the most accurate prediction method for a given problem, it holds a special place in machine learning because even those new to data science can implement and understand this powerful algorithm.

This was the algorithm used in an exciting 2017 study on suicide predictions, conducted by biomedical-informatics specialist Colin Walsh of Vanderbilt University and psychologists Jessica Ribeiro and Joseph Franklin of Florida State University. Their goal was to take what they knew about a set of 5,000 patients with a history of self-injury, and see if they could use those data to predict the likelihood that those patients would commit suicide. The study was done retrospectively. Sadly, almost 2,000 of these patients had killed themselves by the time the research was underway.

Altogether, the researchers had over 1,300 different characteristics they could use to make their predictions, including age, gender, and various aspects of the individuals’ medical histories. If the predictions from the algorithm proved to be accurate, the algorithm could theoretically be used in the future to identify people at high risk of suicide, and deliver targeted programs to them. That would be a very good thing.

Predictive algorithms are everywhere. In an age when data are plentiful and computing power is mighty and cheap, data scientists increasingly take information on people, companies, and markets—whether given willingly or harvested surreptitiously—and use it to guess the future. Algorithms predict what movie we might want to watch next, which stocks will increase in value, and which advertisement we’re most likely to respond to on social media. Artificial-intelligence tools, like those used for self-driving cars, often rely on predictive algorithms for decision making….(More)”.

The Moral Machine experiment

Curated on Posted on by Stefaan Verhulst

Jean-François Bonnefon, Iyad Rahwan et al in Nature:  “With the rapid development of artificial intelligence have come concerns about how machines will make moral decisions, and the major challenge of quantifying societal expectations about the ethical principles that should guide machine behaviour. To address this challenge, we deployed the Moral Machine, an online experimental platform designed to explore the moral dilemmas faced by autonomous vehicles.

This platform gathered 40 million decisions in ten languages from millions of people in 233 countries and territories. Here we describe the results of this experiment. First, we summarize global moral preferences. Second, we document individual variations in preferences, based on respondents’ demographics. Third, we report cross-cultural ethical variation, and uncover three major clusters of countries. Fourth, we show that these differences correlate with modern institutions and deep cultural traits. We discuss how these preferences can contribute to developing global, socially acceptable principles for machine ethics. All data used in this article are publicly available….(More)”.