AI Now 2017 Report

By Alexander Campolo, M. Sanfilippo, Meredith Whittaker, Kate Crawford. 2017

129 citations recorded by the source corpus.

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Oftentimes, however, it is diﬃcult to draw clear boundaries between the more normative connotations and the statistical sense of the term—see Campolo et al. (2017) for a discussion on some of the competing meanings of the term “bias.””
Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases (2020, background): “Artificial intelligence systems are known not only to perpetuate social biases, but they may also amplify existing cultural assumptions and inequalities [2].”
Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics (2020, background): “, product managers, business analysts, computer scientists, ML practitioners — often lack the lived experiences required to comprehensively approximate and account for the various peripheral stakeholders their interventions will impact, especially the communities that are the most vulnerable to unfa”
The Ethics of AI Ethics: An Evaluation of Guidelines (2019, background): “that, it turns out that precisely the reports of AI Now (Whittaker et al. 2018; Campolo et al. 2017; Crawford et al. 2016), an organization primarily led by women, do not conceive AI applications in isolation, but within”
Interpretable Machine Learning (2019, background): “4,31,86 The ICO and Alan Turing Institute have recommended that organisations prioritise using systems that use interpretable ML methods if possible, particularly for applications that have a potentially high impact on a person or are safety critical.”
The global landscape of AI ethics guidelines (2019, background): “…; and (5) via systemic changes and processes such as governmental action 59,62,104,109 and oversight 111 , a more interdisciplinary 64,82,102,110 or otherwise diverse 75,76,87,102,104,112 workforce, as well as better inclusion of civil society or other relevant stakeholders in an…”
GPT-3 and InstructGPT: technological dystopianism, utopianism, and “Contextual” perspectives in AI ethics and industry (2022, background): “Many researchers (e.g., Campolo et al. [7], p. 5, Hagendorff [27, 28], p. 113) have argued that AI ethics must be enforceable beyond the voluntary and non-binding commitments made by industry.”
Bias in machine learning - what is it good for? (2020, background): “The word ‘bias’ has an established normative meaning in legal language, where it refers to ‘judgement based on preconceived notions or prejudices, as opposed to the impartial evaluation of facts’ [8].”
Artificial intelligence and communication: A Human–Machine Communication research agenda (2019, background): “Recent advances in AI have led to more powerful and consequential AI technologies being integrated across daily life (Campolo et al., 2017).”
Ethical governance is essential to building trust in robotics and artificial intelligence systems (2018, background): “Notably, a recent report has recommended that ‘core public agencies ... no longer use “black box” AI and algorithmic systems’ [40].”
A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics (2018, background): “The AI Now Institute identifies “a deep need for interdisciplinary, socially aware work that integrates the long history of bias research from the social sciences and humanities into the field of AI research” (Campolo et al., 2017).”
Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context (2020, background): “However, the fact that researchers interchangeably refer to the concept of social context as the “sociotechnical puzzle” [96], “complex social reality” [16] and “the broader context” [60] illustrates a lack of clarity on what social context is.”
Your evidence? Machine learning algorithms for medical diagnosis and prediction (2019, background): “In the AI Now 2017 Report it is hardly mentioned (Campolo, Sanfilippo, Whittaker, & Crawford, 2017).”
Fairness is not static: deeper understanding of long term fairness via simulation studies (2020, background): “More recently, a number of critical evaluations of the algorithmic fairness literature draw attention to the ways that machine learning systems interact with, and often reinforce, the power structures that generate the inequities that this literature is meant to address [3, 15, 28].”