Liam Tung at ZDNet: “An AI-led, road-safety pilot program between analytics firm Waycare and Nevada transportation agencies has helped reduce crashes along the busy I-15 in Las Vegas.
The Silicon Valley Waycare system uses data from connected cars, road cameras and apps like Waze to build an overview of a city’s roads and then shares that data with local authorities to improve road safety.
Waycare struck a deal with Google-owned Waze earlier this year to “enable cities to communicate back with drivers and warn of dangerous roads, hazards, and incidents ahead”. Waze’s crowdsourced data also feeds into Waycare’s traffic management system, offering more data for cities to manage traffic.
Waycare has now wrapped up a year-long pilot with the Regional Transportation Commission of Southern Nevada (RTC), Nevada Highway Patrol (NHP), and the Nevada Department of Transportation (NDOT).
RTC reports that Waycare helped the city reduce the number of primary crashes by 17 percent along the Interstate 15 Las Vegas.
Waycare’s data, as well as its predictive analytics, gave the city’s safety and traffic management agencies the ability to take preventative measures in high risk areas….(More)”.
Paul R. Daugherty, H. James Wilson, and Rumman Chowdhury at MIT Sloan Management Review: “Artificial intelligence has had some justifiably bad press recently. Some of the worst stories have been about systems that exhibit racial or gender bias in facial recognition applications or in evaluating people for jobs, loans, or other considerations. One program was routinely recommending longer prison sentences for blacks than for whites on the basis of the flawed use of recidivism data.
But what if instead of perpetuating harmful biases, AI helped us overcome them and make fairer decisions? That could eventually result in a more diverse and inclusive world. What if, for instance, intelligent machines could help organizations recognize all worthy job candidates by avoiding the usual hidden prejudices that derail applicants who don’t look or sound like those in power or who don’t have the “right” institutions listed on their résumés? What if software programs were able to account for the inequities that have limited the access of minorities to mortgages and other loans? In other words, what if our systems were taught to ignore data about race, gender, sexual orientation, and other characteristics that aren’t relevant to the decisions at hand?
AI can do all of this — with guidance from the human experts who create, train, and refine its systems. Specifically, the people working with the technology must do a much better job of building inclusion and diversity into AI design by using the right data to train AI systems to be inclusive and thinking about gender roles and diversity when developing bots and other applications that engage with the public.
Design for Inclusion
Software development remains the province of males — only about one-quarter of computer scientists in the United States are women— and minority racial groups, including blacks and Hispanics, are underrepresented in tech work, too. Groups like Girls Who Code and AI4ALL have been founded to help close those gaps. Girls Who Code has reached almost 90,000 girls from various backgrounds in all 50 states,5 and AI4ALL specifically targets girls in minority communities….(More)”.
Paper by Brent Mittelstadt Chris Russell and Sandra Wachter: “Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that “All models are wrong but some are useful.”
We focus on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a “do it yourself kit” for explanations, allowing a practitioner to directly answer “what if questions” or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly… (More)”.
Blog by Giovanni Buttarelli: “…There are few authorities monitoring the impact of new technologies on fundamental rights so closely and intensively as data protection and privacy commissioners. At the International Conference of Data Protection and Privacy Commissioners, the 40th ICDPPC (which the EDPS had the honour to host), they continued the discussion on AI which began in Marrakesh two years ago with a reflection paper prepared by EDPS experts. In the meantime, many national data protection authorities have invested considerable efforts and provided important contributions to the discussion. To name only a few, the data protection authorities from Norway, France, the UKandSchleswig-Holstein have published research and reflections on AI, ethics and fundamental rights. We all see that some applications of AI raise immediate concerns about data protection and privacy; but it also seems generally accepted that there are far wider-reaching ethical implications, as a group of AI researchers also recently concluded. Data protection and privacy commissioners have now made a forceful intervention by adopting a declaration on ethics and data protection in artificial intelligence which spells out six principles for the future development and use of AI – fairness, accountability, transparency, privacy by design, empowerment and non-discrimination – and demands concerted international efforts to implement such governance principles. Conference members will contribute to these efforts, including through a new permanent working group on Ethics and Data Protection in Artificial Intelligence.
The ICDPPC was also chosen by an alliance of NGOs and individuals, The Public Voice, as the moment to launch its own Universal Guidelines on Artificial Intelligence (UGAI). The twelve principles laid down in these guidelines extend and complement those of the ICDPPC declaration.
We are only at the beginning of this debate. More voices will be heard: think tanks such as CIPL are coming forward with their suggestions, and so will many other organisations.
At international level, the Council of Europe has invested efforts in assessing the impact of AI, and has announced a report and guidelines to be published soon. The European Commission has appointed an expert group which will, among other tasks, give recommendations on future-related policy development and on ethical, legal and societal issues related to AI, including socio-economic challenges.
As I already pointed out in an earlier blogpost, it is our responsibility to ensure that the technologies which will determine the way we and future generations communicate, work and live together, are developed in such a way that the respect for fundamental rights and the rule of law are supported and not undermined….(More)”.
Aaron Smith at the Pew Research Center: “Algorithms are all around us, utilizing massive stores of data and complex analytics to make decisions with often significant impacts on humans. They recommend books and movies for us to read and watch, surface news stories they think we might find relevant, estimate the likelihood that a tumor is cancerous and predict whether someone might be a criminal or a worthwhile credit risk. But despite the growing presence of algorithms in many aspects of daily life, a Pew Research Center survey of U.S. adults finds that the public is frequently skeptical of these tools when used in various real-life situations.
This skepticism spans several dimensions. At a broad level, 58% of Americans feel that computer programs will always reflect some level of human bias – although 40% think these programs can be designed in a way that is bias-free. And in various contexts, the public worries that these tools might violate privacy, fail to capture the nuance of complex situations, or simply put the people they are evaluating in an unfair situation. Public perceptions of algorithmic decision-making are also often highly contextual. The survey shows that otherwise similar technologies can be viewed with support or suspicion depending on the circumstances or on the tasks they are assigned to do….
The following are among the major findings.
The public expresses broad concerns about the fairness and acceptability of using computers for decision-making in situations with important real-world consequences
By and large, the public views these examples of algorithmic decision-making as unfair to the people the computer-based systems are evaluating. Most notably, only around one-third of Americans think that the video job interview and personal finance score algorithms would be fair to job applicants and consumers. When asked directly whether they think the use of these algorithms is acceptable, a majority of the public says that they are not acceptable. Two-thirds of Americans (68%) find the personal finance score algorithm unacceptable, and 67% say the computer-aided video job analysis algorithm is unacceptable….
Attitudes toward algorithmic decision-making can depend heavily on context
Despite the consistencies in some of these responses, the survey also highlights the ways in which Americans’ attitudes toward algorithmic decision-making can depend heavily on the context of those decisions and the characteristics of the people who might be affected….
When it comes to the algorithms that underpin the social media environment, users’ comfort level with sharing their personal information also depends heavily on how and why their data are being used. A 75% majority of social media users say they would be comfortable sharing their data with those sites if it were used to recommend events they might like to attend. But that share falls to just 37% if their data are being used to deliver messages from political campaigns.
In other instances, different types of users offer divergent views about the collection and use of their personal data. For instance, about two-thirds of social media users younger than 50 find it acceptable for social media platforms to use their personal data to recommend connecting with people they might want to know. But that view is shared by fewer than half of users ages 65 and older….(More)”.
Report by the World Bank: “…Decisions based on data can greatly improve people’s lives. Data can uncover patterns, unexpected relationships and market trends, making it possible to address previously intractable problems and leverage hidden opportunities. For example, tracking genes associated with certain types of cancer to improve treatment, or using commuter travel patterns to devise public transportation that is affordable and accessible for users, as well as profitable for operators.
Data is clearly a precious commodity, and the report points out that people should have greater control over the use of their personal data. Broadly speaking, there are three possible answers to the question “Who controls our data?”: firms, governments, or users. No global consensus yet exists on the extent to which private firms that mine data about individuals should be free to use the data for profit and to improve services.
User’s willingness to share data in return for benefits and free services – such as virtually unrestricted use of social media platforms – varies widely by country. In addition to that, early internet adopters, who grew up with the internet and are now age 30–40, are the most willing to share (GfK 2017).
Are you willing to share your data? (source: GfK 2017)
On the other hand, data can worsen the digital divide – the data poor, who leave no digital trail because they have limited access, are most at risk from exclusion from services, opportunities and rights, as are those who lack a digital ID, for instance.
Firms and Data
For private sector firms, particularly those in developing countries, the report suggests how they might expand their markets and improve their competitive edge. Companies are already developing new markets and making profits by analyzing data to better understand their customers. This is transforming conventional business models. For years, telecommunications has been funded by users paying for phone calls. Today, advertisers pay for users’ data and attention are funding the internet, social media, and other platforms, such as apps, reversing the value flow.
Governments and Data
For governments and development professionals, the report provides guidance on how they might use data more creatively to help tackle key global challenges, such as eliminating extreme poverty, promoting shared prosperity, or mitigating the effects of climate change. The first step is developing appropriate guidelines for data sharing and use, and for anonymizing personal data. Governments are already beginning to use the huge quantities of data they hold to enhance service delivery, though they still have far to go to catch up with the commercial giants, the report finds.
Data for Development
The Information and Communications for Development report analyses how the data revolution is changing the behavior of governments, individuals, and firms and how these changes affect economic, social, and cultural development. This is a topic of growing importance that cannot be ignored, and the report aims to stimulate wider debate on the unique challenges and opportunities of data for development. It will be useful for policy makers, but also for anyone concerned about how their personal data is used and how the data revolution might affect their future job prospects….(More)”.
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)”.
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:
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)”.
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)”.
