Machine Learning, Big Data and the Regulation of Consumer Credit Markets: The Case of Algorithmic Credit Scoring

Curated on Posted on by Stefaan Verhulst

Paper by Nikita Aggarwal et al: “Recent advances in machine learning (ML) and Big Data techniques have facilitated the development of more sophisticated, automated consumer credit scoring models — a trend referred to as ‘algorithmic credit scoring’ in recognition of the increasing reliance on computer (particularly ML) algorithms for credit scoring. This chapter, which forms part of the 2018 collection of short essays ‘Autonomous Systems and the Law’, examines the rise of algorithmic credit scoring, and considers its implications for the regulation of consumer creditworthiness assessment and consumer credit markets more broadly.

The chapter argues that algorithmic credit scoring, and the Big Data and ML technologies underlying it, offer both benefits and risks for consumer credit markets. On the one hand, it could increase allocative efficiency and distributional fairness in these markets, by widening access to, and lowering the cost of, credit, particularly for ‘thin-file’ and ‘no-file’ consumers. On the other hand, algorithmic credit scoring could undermine distributional fairness and efficiency, by perpetuating discrimination in lending against certain groups and by enabling the more effective exploitation of borrowers.

The chapter considers how consumer financial regulation should respond to these risks, focusing on the UK/EU regulatory framework. As a general matter, it argues that the broadly principles and conduct-based approach of UK consumer credit regulation provides the flexibility necessary for regulators and market participants to respond dynamically to these risks. However, this approach could be enhanced through the introduction of more robust product oversight and governance requirements for firms in relation to their use of ML systems and processes. Supervisory authorities could also themselves make greater use of ML and Big Data techniques in order to strengthen the supervision of consumer credit firms.

Finally, the chapter notes that cross-sectoral data protection regulation, recently updated in the EU under the GDPR, offers an important avenue to mitigate risks to consumers arising from the use of their personal data. However, further guidance is needed on the application and scope of this regime in the consumer financial context….(More)”.

The wisdom of crowds: What smart cities can learn from a dead ox and live fish

Curated on Posted on by Stefaan Verhulst

Portland State University: “In 1906, Francis Galton was at a country fair where attendees had the opportunity to guess the weight of a dead ox. Galton took the guesses of 787 fair-goers and found that the average guess was only one pound off of the correct weight — even when individual guesses were off base.

This concept, known as “the wisdom of crowds” or “collective intelligence,” has been applied to many situations over the past century, from people estimating the number of jellybeans in a jar to predicting the winners of major sporting events — often with high rates of success. Whatever the problem, the average answer of the crowd seems to be an accurate solution.

But does this also apply to knowledge about systems, such as ecosystems, health care, or cities? Do we always need in-depth scientific inquiries to describe and manage them — or could we leverage crowds?

This question has fascinated Antonie J. Jetter, associate professor of Engineering and Technology Management for many years. Now, there’s an answer. A recent study, which was co-authored by Jetter and published in Nature Sustainability, shows that diverse crowds of local natural resource stakeholders can collectively produce complex environmental models very similar to those of trained experts.

For this study, about 250 anglers, water guards and board members of German fishing clubs were asked to draw connections showing how ecological relationships influence the pike stock from the perspective of the anglers and how factors like nutrients and fishing pressures help determine the number of pike in a freshwater lake ecosystem. The individuals’ drawings — or their so-called mental models — were then mathematically combined into a collective model representing their averaged understanding of the ecosystem and compared with the best scientific knowledge on the same subject.

The result is astonishing. If you combine the ideas from many individual anglers by averaging their mental models, the final outcomes correspond more or less exactly to the scientific knowledge of pike ecology — local knowledge of stakeholders produces results that are in no way inferior to lengthy and expensive scientific studies….(More)”.

Collective Intelligence in City Design

Curated on Posted on by Stefaan Verhulst

Idea by Helena Rong and Juncheng Yang: “We propose an interactive design engagement platform which facilitates a continuous conversation between developers, designers and end users from pre-design and planning phases all the way to post-occupancy, adopting a citizen-centric and inclusive-oriented approach which would stimulate trust-building and invite active participation from end users from different age, ethnicity, social and economic background to participate in the design and development process. We aim to explore how collective intelligence through citizen engagement could be enabled by data to allow new collectives to emerge, confronting design as an iterative process involving scalable cooperation of different actors. As a result, design is a collaborative and conscious practice not born out of a single mastermind of the architect. Rather, its agency is reinforced by a cooperative ideal involving institutions, enterprises and single individuals alike enabled by data science….(More)”

The Wild Wild West of Data Hoarding in the Federal Government

Curated on Posted on by Stefaan Verhulst

ActiveNavigation: “There is a strong belief, both in the public and private sector, that the worst thing you can do with a piece of data is to delete it. The government stores all sorts of data, from traffic logs to home ownership statistics. Data is obviously incredibly important to the Federal Government – but storing large amounts of it poses significant compliance and security risks – especially with the rise of Nation State hackers. As the risk of being breached continues to rise, why is the government not tackling their data storage problem head on?

The Myth of “Free” Storage

Storage is cheap, especially compared to 10-15 years ago. Cloud storage has made it easier than ever to store swaths of information, creating what some call “digital landfills.” However, the true cost of storage isn’t in the ones and zeros sitting on the server somewhere. It’s the business cost.

As information stores continue to grow, the Federal Government’s ability to execute moving information to the correct place gets harder and harder, not to mention more expensive. The U.S. Government has a duty to provide accurate, up-to-date information to its taxpayers – meaning that sharing “bad data” is not an option.

The Association of Information and Image Management (AIIM) reports that half of an organization’s retained data has no value. So far, in 2019, through our work with Federal Agencies, we have discovered that this number, is in fact, low. Over 66% of data we’ve indexed, by the client’s definition, has fallen into that “junk” category. Eliminating junk data paves the way for greater accessibility, transparency and major financial savings. But what is “junk” data?

Redundant, Obsolete and Trivial (ROT) Data

Data is important – but if you can’t assign a value to it, it can become impossible to manage. Simply put, ROT data is digital information that an organization retains, that has no business or legal value. To be efficient from both a cyber hygiene and business perspective, the government needs to get better at purging their ROT data.

Again, purging data doesn’t just help with the hard cost of storage and backups, etc. For example, think about what needs to be done to answer a Freedom of Information Act (FOIA) request. You have a petabyte of data. You have at least a billion documents you need to funnel through to be able to respond to that FOIA request. By eliminating 50% of your ROT data, you probably have also reduced your FOIA response time by 50%.

Records and information governance, taken at face value, might seem fairly esoteric. It may not be as fun or as sexy as the new Space Force, but the reality is, the only way to know if the government is doing what it says it’s through records and information. You can’t answer an FOIA request if there’s no material. You can’t answer Congress if the material isn’t accurate. Being able to access timely, accurate information is critical. That’s why NARA is advocating a move to electronic records.…(More)”.

The future is intelligent: Harnessing the potential of artificial intelligence in Africa

Curated on Posted on by Stefaan Verhulst

Youssef Travaly and Kevin Muvunyi at Brookings: “…AI in particular presents countless avenues for both the public and private sectors to optimize solutions to the most crucial problems facing the continent today, especially for struggling industries. For example, in health care, AI solutions can help scarce personnel and facilities do more with less by speeding initial processing, triage, diagnosis, and post-care follow up. Furthermore, AI-based pharmacogenomics applications, which focus on the likely response of an individual to therapeutic drugs based on certain genetic markers, can be used to tailor treatments. Considering the genetic diversity found on the African continent, it is highly likely that the application of these technologies in Africa will result in considerable advancement in medical treatment on a global level.

In agriculture, Abdoulaye Baniré Diallo, co-founder and chief scientific officer of the AI startup My Intelligent Machines, is working with advanced algorithms and machine learning methods to leverage genomic precision in livestock production models. With genomic precision, it is possible to build intelligent breeding programs that minimize the ecological footprint, address changing consumer demands, and contribute to the well-being of people and animals alike through the selection of good genetic characteristics at an early stage of the livestock production process. These are just a few examples that illustrate the transformative potential of AI technology in Africa.

However, a number of structural challenges undermine rapid adoption and implementation of AI on the continent. Inadequate basic and digital infrastructure seriously erodes efforts to activate AI-powered solutions as it reduces crucial connectivity. (For more on strategies to improve Africa’s digital infrastructure, see the viewpoint on page 67 of the full report). A lack of flexible and dynamic regulatory systems also frustrates the growth of a digital ecosystem that favors AI technology, especially as tech leaders want to scale across borders. Furthermore, lack of relevant technical skills, particularly for young people, is a growing threat. This skills gap means that those who would have otherwise been at the forefront of building AI are left out, preventing the continent from harnessing the full potential of transformative technologies and industries.

Similarly, the lack of adequate investments in research and development is an important obstacle. Africa must develop innovative financial instruments and public-private partnerships to fund human capital development, including a focus on industrial research and innovation hubs that bridge the gap between higher education institutions and the private sector to ensure the transition of AI products from lab to market….(More)”.

Data Democracy

Curated on Posted on by Stefaan Verhulst

Book by Feras Batarseh and Ruixin Yang: “Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering provides a manifesto to data democracy. After reading the chapters of this book, you are informed and suitably warned! You are already part of the data republic, and you (and all of us) need to ensure that our data fall in the right hands. Everything you click, buy, swipe, try, sell, drive, or fly is a data point. But who owns the data? At this point, not you! You do not even have access to most of it. The next best empire of our planet is one who owns and controls the world’s best dataset. If you consume or create data, if you are a citizen of the data republic (willingly or grudgingly), and if you are interested in making a decision or finding the truth through data-driven analysis, this book is for you. A group of experts, academics, data science researchers, and industry practitioners gathered to write this manifesto about data democracy.

Key Features

  • The future of the data republic, life within a data democracy, and our digital freedoms
  • An in-depth analysis of open science, open data, open source software, and their future challenges
  • A comprehensive review of data democracy’s implications within domains such as: healthcare, space exploration, earth sciences, business, and psychology
  • The democratization of Artificial Intelligence (AI), and data issues such as: Bias, imbalance, context, and knowledge extraction
  • A systematic review of AI methods applied to software engineering problems…(More)”.

Crossing the Digital Divide: Applying Technology to the Global Refugee Crisis

Curated on Posted on by Stefaan Verhulst

Report by Shelly Culbertson, James Dimarogonas, Katherine Costello, and Serafina Lanna: “In the past two decades, the global population of forcibly displaced people has more than doubled, from 34 million in 1997 to 71 million in 2018. Amid this growing crisis, refugees and the organizations that assist them have turned to technology as an important resource, and technology can and should play an important role in solving problems in humanitarian settings. In this report, the authors analyze technology uses, needs, and gaps, as well as opportunities for better using technology to help displaced people and improving the operations of responding agencies. The authors also examine inherent ethical, security, and privacy considerations; explore barriers to the successful deployment of technology; and outline some tools for building a more systematic approach to such deployment. The study approach included a literature review, semi-structured interviews with stakeholders, and focus groups with displaced people in Colombia, Greece, Jordan, and the United States. The authors provide several recommendations for more strategically using and developing technology in humanitarian settings….(More)”.

How people decide what they want to know

Curated on Posted on by Stefaan Verhulst

Tali Sharot & Cass R. Sunstein in Nature: “Immense amounts of information are now accessible to people, including information that bears on their past, present and future. An important research challenge is to determine how people decide to seek or avoid information. Here we propose a framework of information-seeking that aims to integrate the diverse motives that drive information-seeking and its avoidance. Our framework rests on the idea that information can alter people’s action, affect and cognition in both positive and negative ways. The suggestion is that people assess these influences and integrate them into a calculation of the value of information that leads to information-seeking or avoidance. The theory offers a framework for characterizing and quantifying individual differences in information-seeking, which we hypothesize may also be diagnostic of mental health. We consider biases that can lead to both insufficient and excessive information-seeking. We also discuss how the framework can help government agencies to assess the welfare effects of mandatory information disclosure….(More)”.

Technology Can't Fix Algorithmic Injustice

Curated on Posted on by Stefaan Verhulst

Annette Zimmerman, Elena Di Rosa and Hochan Kima at Boston Review: “A great deal of recent public debate about artificial intelligence has been driven by apocalyptic visions of the future. Humanity, we are told, is engaged in an existential struggle against its own creation. Such worries are fueled in large part by tech industry leaders and futurists, who anticipate systems so sophisticated that they can perform general tasks and operate autonomously, without human control. Stephen Hawking, Elon Musk, and Bill Gates have all publicly expressed their concerns about the advent of this kind of “strong” (or “general”) AI—and the associated existential risk that it may pose for humanity. In Hawking’s words, the development of strong AI “could spell the end of the human race.”

These are legitimate long-term worries. But they are not all we have to worry about, and placing them center stage distracts from ethical questions that AI is raising here and now. Some contend that strong AI may be only decades away, but this focus obscures the reality that “weak” (or “narrow”) AI is already reshaping existing social and political institutions. Algorithmic decision making and decision support systems are currently being deployed in many high-stakes domains, from criminal justice, law enforcement, and employment decisions to credit scoring, school assignment mechanisms, health care, and public benefits eligibility assessments. Never mind the far-off specter of doomsday; AI is already here, working behind the scenes of many of our social systems.

What responsibilities and obligations do we bear for AI’s social consequences in the present—not just in the distant future? To answer this question, we must resist the learned helplessness that has come to see AI development as inevitable. Instead, we should recognize that developing and deploying weak AI involves making consequential choices—choices that demand greater democratic oversight not just from AI developers and designers, but from all members of society….(More)”.

Experimenting with Public Engagement Platforms in Local Government

Curated on Posted on by Stefaan Verhulst

Paper by Seongkyung Cho et al: “Cities are venues for experimentation with technology (e.g., smart cities) and democratic governance. At the intersection of both trends is the emergence of new online platforms for citizen engagement. There is little evidence to date on the extent to which these are being used or the characteristics associated with adopters at the leading edge. With rich data on civic engagement and innovation from a 2016 International City/County Management Association (ICMA) survey, we explore platform use in U.S. local governments and relationships with offline civic engagement, innovation, and local characteristics. We find that use of online participatory platforms is associated with offline participation, goals for civic engagement, and city size, rather than evidence that this is related to a more general orientation toward innovation….(More)”.