政治自由主义 = Political liberalism

Frantz Fanon’s explosive Black Skin, White Masks is a merciless expose of the psychological damage done by colonial rule across the world. Using Fanon’s incisive analytical abilities to expose the consequences of colonialism on the psyches of colonized peoples, it is both a crucial text in post-colonial theory, and a lesson in the power of analytical skills to reveal the realities that hide beneath the surface of things.  
Fanon was himself part of a colonized nation – Martinique – and grew up with the values and beliefs of French culture imposed upon him, while remaining relegated to an inferior status in society. Qualifying as a psychiatrist in France before working in Algeria (a French colony subject to brutal repression), his own experiences granted him a sharp insight into the psychological problems associated with colonial rule.  
Like any good analytical thinker, Fanon’s particular skill was in breaking things down and joining dots. His analysis of colonial rule exposed its implicit assumptions – and how they were replicated in colonised populations – allowing Fanon to unpick the hidden reasons behind his own conflicted psychological make up, and those of his patients. Unflinchingly clear-sighted in doing so, Black Skin White Masks remains a shocking read today.

An Analysis of Frantz Fanon's Black Skin, White Masks

In the past five years, private companies, research institutions and public sector organizations have issued principles and guidelines for ethical artificial intelligence (AI). However, despite an apparent agreement that AI should be ‘ethical’, there is debate about both what constitutes ‘ethical AI’ and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analysed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted, why they are deemed important, what issue, domain or actors they pertain to, and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

The global landscape of AI ethics guidelines

Justice and the Politics of Difference

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

Artificial Intelligence: the global landscape of ethics guidelines.

Problems of bias and fairness are central to data justice, as they speak directly to the threat that ‘big data’ and algorithmic decision-making may worsen already existing injustices. In the United...

Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse

This paper uses frame analysis to examine recent high-profile values statements endorsing ethical design for artificial intelligence and machine learning (AI/ML). Guided by insights from values in design and the sociology of business ethics, we uncover the grounding assumptions and terms of debate that make some conversations about ethical design possible while forestalling alternative visions. Vision statements for ethical AI/ML co-opt the language of some critics, folding them into a limited, technologically deterministic, expert-driven view of what ethical AI/ML means and how it might work.

/pdf/better-nicer-clearer-fairer-a-critical-assessment-of-the-34puc5vvmh.pdf

Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning

Despite the participatory and democratic promises of Web 2.0, many marginalized individuals with fluid or non-normative identities continue to struggle to represent themselves online. Facebook users, in particular, are told to use “authentic identities,” an idea reinforced throughout the site’s documentation, “real name” and other policies, and in public statements by company representatives. Facebook’s conception of authenticity and real names, however, has created problems for certain users, as demonstrated by the systematic deactivation of many accounts belonging to transgender and gender variant users, drag queens, Native Americans, abuse survivors, and others. In view of the struggles of marginalized users, Facebook policy appears paradoxical: the site simultaneously demands authenticity yet proscribes certain people from authentic self-presentation. In this work, we examine Facebook’s construction of “authenticity” and show how it excludes multifaceted, fluid, or non-normative identities. Using content analysis and close reading, we analyze site documentation and data from The Zuckerberg Files (an online archive of Facebook founder and CEO Mark Zuckerberg’s public remarks) to understand the platform’s mechanisms for enforcing authenticity. We find that Facebook positions itself as a type of administrative identity registrar, raising vital questions regarding the ethics and consequences of identity enforcement online today.

Constructing and enforcing "authentic" identity online: Facebook, real names, and non-normative identities

Inclusion has emerged as an early cornerstone value for the emerging domain of “data ethics.” On the surface, appeals to inclusion appear to address the threat that biased data technologies making ...

Terms of inclusion: Data, discourse, violence:

The dominance of online social networking sites (SNSs) sparks questions and concerns regarding information privacy, online identity, and the complexities of social life online. Since messages created by a technology’s purveyors can play an influential role in our understanding of a technology, we argue that gaining a complete understanding of the role of social media in contemporary life must include qualitative exploration of how public figures discuss and frame these platforms. Accordingly, this article reports the results of a discourse analysis of Facebook founder and CEO Mark Zuckerberg’s public language, foregrounding the evolution of his discourse surrounding Facebook’s self-definitions, the construction of user identity, and the relationship between Facebook and its users.

Anna Lauren Hoffmann

Papers

Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse

Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning

Constructing and enforcing "authentic" identity online: Facebook, real names, and non-normative identities

Terms of inclusion: Data, discourse, violence:

“Making the world more open and connected”: Mark Zuckerberg and the discursive construction of Facebook and its users