Are we still doing the work it takes to make positive and impactful change? Are we continuing the work to break down systems, policies, and unwritten industry rules that are no longer fit for purpose?
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Part two of an introduction to two new toolkits from C4DISC — today a look at the Antiracism Toolkit for Black, Indigenous, and People of Color.
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The first of a two part series introducing new toolkits from C4DISC: Guidelines on Inclusive Language and Images in Scholarly Communication and the Antiracism Toolkit for Black, Indigenous, and People of Color.
The post Guest Post — Introducing Two New Toolkits to Advance Inclusion in Scholarly Communication: Part 1 appeared first on The Scholarly Kitchen.
Another “mixed bag” post from us — Is it time to leave Twitter? How can we incentivize journals and authors to take up open science practices? What is “involution” and is DEIA the solution?
The post Smorgasbord: Twitter v. Mastodon; Incentivizing Open Science; DEI v. Involution appeared first on The Scholarly Kitchen.
An interview with Gunay Kazimzade (Weizenbaum Institute for the Networked Society – The German Internet Institute)
Typically, biases occur in all forms of discrimination in our society, such as political, cultural, financial, or sexual. These are again manifested in the data sets collected and the structures and infrastructures around the data, technology, and society, and thus represent social standards and decision-making behaviour in particular data points. AI systems trained upon those data points show prejudices in various domains and applications.
For instance, facial recognition systems built upon biased data tend to discriminate against people of colour in several computer vision applications. According to research from MIT Media Lab, white male and black female accuracy differ dramatically in vision models. In 2018, Amazon “killed” its hiring system, which has started to eliminate female candidates for engineering and high-level positions. This outcome resulted from the company’s culture to prefer male candidates to females in those particular positions traditionally. These examples clarify that AI systems are not objective and are mapping human biases we have in society to the technological level.
Bias is an unavoidable consequence of situated decision-making. The decision of who and how classifies data, which data points are included in the system, is not new to libraries’ work. Libraries and archives are not just the data storage, processing, and access providers. They are critical infrastructures committed to making information available and discoverable yet with the desirable vision to eliminate discriminatory outcomes of those data points.
Imagine a situation where researchers approach the library asking for images to train a face recognition model. The quality and diversity of this data directly impact the results of the research and system developed upon those data. Diversity in images (Youtube) has been recently investigated in the “Gender shades” study by Joy Buolamwini from MIT Media Lab. The question here is: Could library staff identify demographic bias in the data sets before the Gender Shades study was published? Probably not.
The right mindset comes from awareness. Awareness is the social responsibility and self-determination framed with the critical library skills and subject specialization. Relying only on metadata would not be necessary for eliminating bias in data collections. Diversity in staffing and critical domain-specific skills and tools are crucial assets in analysing library system digitised collections. Training of library staffing, continuous training, and evaluation should be the primary strategy of the libraries on the way to detect, understand and mitigate biases in library information systems.
Whether it is a developer, user, provider, or another stakeholder, the right mindset starts with the
Therefore, long-term strategy for library information systems management should include
Several research findings suggest making recommendations fairer and out of the “filter bubbles” created by technology deployers. In recommendations, transparency and explainability are among the main techniques for approaching this problem. Developers should consider the explainability of the suggestions made by the algorithms and make the recommendations justifiable for the user of the system. It should be transparent for the user based on which criteria this particular book recommendation was made and whether it was based on gender, race, or other sensitive attributes. Library or digital infrastructure staff are the main actors in this technology deployment pipeline. They should be conscious and reinforce the decision-makers to deploy the technology that includes the specific features for explainability and transparency in the library systems.
First, “check-up” should start by verifying the quality of the data through quantitative and qualitative, mixed experimental methods. In addition, there are several open-access methodologies and tools for fairness check and bias detection/mitigation in several domains. For instance, AI Fairness 360 is an open-source toolkit that helps to examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle.
Another useful tool is “Datasheets for datasets”, intended to document the datasets used for training and evaluating machine learning models; this tool is very relevant in developing metadata for library and archive systems, which can be further used for model training.
Overall, everything starts with the right mindset and awareness on approaching the bias challenge in specific domains.
Further Readings
We were talking to:
Gunay Kazimzade is a Doctoral Researcher in Artificial Intelligence at the Weizenbaum Institute for the Networked Society in Berlin, where she is currently working with the research group “Criticality of AI-based Systems”. She is also a PhD candidate in Computer Science at the Technical University of Berlin. Her main research directions are gender and racial bias in AI, inclusivity in AI, and AI-enhanced education. She is a TEDx speaker, Presidential Award of Youth winner in Azerbaijan and AI Newcomer Award winner in Germany. Gunay Kazimzade can also be found on Google Scholar, ResearchGate und LinkedIn.
Portrait: Weizenbaum Institute©
The post Discrimination Through AI: To What Extent Libraries are Affected and how Staff can Find the Right Mindset first appeared on ZBW MediaTalk.
France is organising a major international event in the context of the French Presidency of the European Union. This international conference is being organised with the strong support of the Ministry […]
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Learn how two early career publishers are tackling the thorny issue of pay equity and inclusion in today’s interview with Rebecca Bostock (Ohio State UP) and Dominique J Moore (University of Illinois Press)
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Laura Norton and Nicola Nugent of the Royal Society of Chemistry answer Alice Meadows’s questions about the RSC’s Joint Commitment for action on inclusion and diversity in publishing
The post Joint Commitment for Action on Inclusion and Diversity in Publishing: An Interview with Laura Norton and Nicola Nugent of the RSC appeared first on The Scholarly Kitchen.