Walking Before Running? Debiasing and the Regulation of Artificial Intelligence

This new report titled “Beyond Debiasing: Regulating AI and its inequalities” and commissioned by European Digital Rights (EDRi), an association of civil and human rights organizations from across Europe, outlines the limits of technical debiasing measures as a solution to structural discrimination and inequality through AI systems. It shows the vast impact of AI-based systems on the governance, operations and financial stability of public sector organizations, further embedding the dominance of technology companies.

en flag
nl flag
et flag
fi flag
fr flag
de flag
he flag
ja flag
lv flag
pl flag
pt flag
ru flag
es flag

Content Assessment: Beyond Debiasing? Regulating AI and Its Inequalities

Information - 95%
Insight - 100%
Relevance - 90%
Objectivity - 85%
Authority - 90%

92%

Excellent

A short percentage-based assessment of the qualitative benefit of the EDRI published paper on a technical debiasing in the use of artificial intelligence.

Editor’s Note: From time to time, ComplexDiscovery highlights publicly available or privately purchasable announcements, content updates, and research from cyber, data, and legal discovery providers, research organizations, and ComplexDiscovery community members. While ComplexDiscovery regularly highlights this information, it does not assume any responsibility for content assertions.

To submit recommendations for consideration and inclusion in ComplexDiscovery’s cyber, data, and legal discovery-centric service, product, or research announcements, contact us today.


Research Report*

Beyond Debiasing: Regulating AI and Its Inequalities

Citation: Balayn, A. and Gürses, S., 2021. Beyond Debiasing? Regulating AI and Its Inequalities. [online] Brussels: EDRI. Available at: <https://edri.org/wp-content/uploads/2021/09/EDRi_Beyond-Debiasing-Report_Online.pdf> [Accessed 23 September 2021].

Executive Summary Extract

AI-driven systems have broad social and economic impacts and demonstrably exacerbate structural discrimination and inequalities. For the most part, regulators have responded by narrowly focusing on the techno-centric solution of debiasing algorithms and datasets. By doing so, regulators risk creating a bigger problem for both AI governance and democracy because this narrow approach squeezes complex socio-technical problems into the domain of design and thus into the hands of technology companies. By largely ignoring the costly production environments that machine learning requires, regulators condone an expansionist model of computational infrastructures (clouds, mobile phones, and sensor networks) driven by Big Tech. Effective solutions require bold regulations that target the root of power imbalances inherent to the pervasive deployment of AI-driven systems.

Report Commentary Extract: Analyzing AI Systems

Even if policymakers develop a better grasp of the technical methods of debiasing data or algorithms, debiasing approaches will not effectively address the discriminatory impact of AI systems. By design, debiasing approaches concentrate power in the hands of service providers, giving them (and not lawmakers) the discretion to decide what counts as discrimination, when it occurs and how to address it.

The report unpacks the problematic assumptions about AI and offers an assessment of the limits of a focus on debiasing. The report puts forward alternative viewpoints that go beyond current techno-centric debates on data, algorithms and automated decision-making systems (ADMs). These frameworks outline different ways (views) of analyzing AI systems’ societal impact, yet are currently missing in policy debates on ‘bias’. These ways (views) include:

  • Machine Learning View: Aspects inherent to the fundamental principles of machine learning (such as the repetition of past data patterns, targeted inferences, inherent tendency to increase scale) are likely to pose harms which are often not considered in debiasing debates.
  • Production View: The focus on AI as a set ‘product’ obscures the complex processes by which AI systems are integrated into broader environments, which often create significant harms (such as labour exploitation, environmental extraction) often overlooked by policymakers.
  • Infrastructural View: The production and deployment of machine learning is heavily dependent on existing computational infrastructures in the hands of a few companies. Ownership over these computational resources is likely to lead to greater concentration of the technical, financial and political power of technology companies, exacerbating global concerns around political, economic and social inequalities.
  • Organizational View: AI based systems offer organizations the possibility to automate and centralize workflows and optimize institutional management and operations. These transformations are likely to bring about dependencies on third-parties and computational infrastructures, with demonstrable consequences for the structure of the public sector and democracy more generally.

Read the complete report commentary from the original source.


Complete Report: Beyond Debiasing? Regulating AI and Its Inequalities (PDF) – Mouseover to Scroll

EDRi Beyond Debiasing Report

Access the original report.

*Shared with permission of EDRi under Creative Commons – Attribution 4.0 International (CC BY 4.0) – license. European Digital Rights (EDRi) is an association of civil and human rights organizations from across Europe focused on rights and freedoms in digital environments.


Additional Reading

Source: ComplexDiscovery

Have a Request?

If you have information or offering requests that you would like to ask us about, please let us know and we will make our response to you a priority.

ComplexDiscovery is an online publication that highlights cyber, data and legal discovery insight and intelligence ranging from original research to aggregated news for use by cybersecurity, information governance, and eDiscovery professionals. The highly targeted publication seeks to increase the collective understanding of readers regarding cyber, data and legal discovery information and issues and to provide an objective resource for considering trends, technologies, and services related to electronically stored information.

ComplexDiscovery OÜ is a technology marketing firm providing strategic planning and tactical execution expertise in support of cyber, data and legal discovery organizations. Focused primarily on supporting the ComplexDiscovery publication, the company is registered as a private limited company in the European Union country of Estonia, one of the most digitally advanced countries in the world. The company operates virtually worldwide to deliver marketing consulting and services.

From Russia (and China) with Love? The UK National Cyber Security Centre Annual Review

According to the NCSC Annual Review, China remained a highly sophisticated...

New Federal Government Cybersecurity Incident and Vulnerability Response Playbooks

According to Matt Hartman, Deputy Executive Assistant Director for Cybersecurity, "The...

A Surge in Cybercriminality? The Annual ENISA Threat Landscape Report – 9th Edition

According to EU Agency for Cybersecurity Executive Director Juhan Lepassaar, “Given...

Considering Zero Trust? November 2021 Cyber Events Report from NATO CCDCOE

Computer security professionals love to say that there is no such...

A Long Runway? KLDiscovery Files for Initial Public Offering

On Tuesday, November 23, 2021, KLDiscovery took a strong step toward...

Modus Secures Working Capital Facility from J.P. Morgan

According to Steven Horan, Chairman, and CEO of Modus, “Having the...

Driven and Innovative Discovery Merge

According to the announcement, Silver Oak Services Partners, a private equity...

Smarsh Acquires Digital Safe Product Line from Micro Focus

According to Smarsh CEO Brian Cramer, “Solving the sophisticated archiving, compliance...

An eDiscovery Market Size Mashup: 2021-2026 Worldwide Software and Services Overview

From market retraction in 2020 to resurgence in 2021, the worldwide...

A New Era in eDiscovery? Framing Market Growth Through the Lens of Six Eras

There are many excellent resources for considering chronological and historiographical approaches...

An eDiscovery Market Size Mashup: 2020-2025 Worldwide Software and Services Overview

While the Compound Annual Growth Rate (CAGR) for worldwide eDiscovery software...

Resetting the Baseline? eDiscovery Market Size Adjustments for 2020

An unanticipated pandemeconomic-driven retraction in eDiscovery spending during 2020 has resulted...

Five Great Reads on Cyber, Data, and Legal Discovery for November 2021

From worldwide eDiscovery market sizing and discovery intelligence to cybersecurity playbooks...

Five Great Reads on Cyber, Data, and Legal Discovery for October 2021

From artificial intelligence and predictive coding to eDiscovery business confidence and...

Five Great Reads on Cyber, Data, and Legal Discovery for September 2021

From countering ransomware to predictive coding and packaged services, the September...

Five Great Reads on Cyber, Data, and Legal Discovery for August 2021

From the interplay of digital forensics in eDiscovery to collecting online...

Calm Before the Storm? Eighteen Observations on eDiscovery Business Confidence in the Fall of 2021

In the fall of 2021, 71.2% of survey respondents felt that...

Help Wanted? Issues Impacting eDiscovery Business Performance: A Fall 2021 Overview

In the fall of 2021, 27.4% of respondents viewed lack of...

Harvest Time? eDiscovery Operational Metrics in the Fall of 2021

In the fall of 2021, 67 eDiscovery Business Confidence Survey participants...

Unseasonably Hot? Fall 2021 eDiscovery Business Confidence Survey Results

Since January 2016, 2,595 individual responses to twenty-four quarterly eDiscovery Business...