Assessing and addressing algorithmic bias in practice

Cramer, Henriette; Garcia-Gathright, Jean; Springer, Aaron; Reddy, Sravana

doi:10.1145/3278156

Cited by 48 publications

(51 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this framing, the solution is to ensure that each of the individual steps is performed fairly. However, many issues such as complex system interactions and broader ethical questions often fall the practice of sharing insights from industrial settings (e.g., [3]), in this article I will share my perspective on the nature of the problem space. My hope is that a richer understanding of on-the-ground practicalities may help those working in this area target high-leverage problems and maximize the effectiveness of their efforts to identify, remediate, and prevent potential unfairness.…”

Section: Big Gap Between Idealized Process and Problems In Practicementioning

confidence: 99%

10 things you should know about algorithmic fairness

Woodruff

2019

interactions

View full text Add to dashboard Cite

Section: Big Gap Between Idealized Process and Problems In Practicementioning

confidence: 99%

10 things you should know about algorithmic fairness

Woodruff

2019

interactions

View full text Add to dashboard Cite

“…It is widespread common sense that AI-supported technologies should be used for a common good. AIsupported technologies should not be used to harm or undermine anyone, and should respect widely held values such as fairness, privacy, and autonomy (Cramer et al, 2018). The amount of publications and standardizations in the field of ethical AI in general is continuously emerging.…”

Section: Ai Ethicsmentioning

confidence: 99%

Ethical Questions Raised by AI-Supported Mentoring in Higher Education

Köbis

Mehner

2021

Front. Artif. Intell.

View full text Add to dashboard Cite

Mentoring is a highly personal and individual process, in which mentees take advantage of expertise and experience to expand their knowledge and to achieve individual goals. The emerging use of AI in mentoring processes in higher education not only necessitates the adherence to applicable laws and regulations (e.g., relating to data protection and non-discrimination) but further requires a thorough understanding of ethical norms, guidelines, and unresolved issues (e.g., integrity of data, safety, and security of systems, and confidentiality, avoiding bias, insuring trust in and transparency of algorithms). Mentoring in Higher Education requires one of the highest degrees of trust, openness, and social–emotional support, as much is at the stake for mentees, especially their academic attainment, career options, and future life choices. However, ethical compromises seem to be common when digital systems are introduced, and the underlying ethical questions in AI-supported mentoring are still insufficiently addressed in research, development, and application. One of the challenges is to strive for privacy and data economy on the one hand, while Big Data is the prerequisite of AI-supported environments on the other hand. How can ethical norms and general guidelines of AIED be respected in complex digital mentoring processes? This article strives to start a discourse on the relevant ethical questions and in this way raise awareness for the ethical development and use of future data-driven, AI-supported mentoring environments in higher education.

show abstract

“…A number of researchers have identified limitations of automated transcription software, including the impact of noise levels on accuracy, for example, McGurk et al (2008); the inability to distinguish between different voices, for example, Matheson (2007); and multiple biases affecting software performance, for example, Cramer et al (2018), Howard and Borenstein (2018) and Tatman (2016). 4 Both Moore (2015) and Matheson (2007) identified the need for 'training' the software involved, both initially and on an ongoing basis, which is not a necessity with the software utilised by Bokhove and Downey (2018) or with that used in the method I relate below.…”

Section: Related Workmentioning

confidence: 99%

Producing ‘good enough’ automated transcripts securely: Extending Bokhove and Downey (2018) to address security concerns

Silva

2021

Methodological Innovations

View full text Add to dashboard Cite

Interviews are an established research method across multiple disciplines. Such interviews are typically transcribed orthographically in order to facilitate analysis. Many novice qualitative researchers’ experiences of manual transcription are that it is tedious and time-consuming, although it is generally accepted within much of the literature that quality of analysis is improved through researchers performing this task themselves. This is despite the potential for the exhausting nature of bulk transcription to conversely have a negative impact upon quality. Other researchers have explored the use of automated methods to ease the task of transcription, more recently using cloud-computing services, but such services present challenges to ensuring confidentiality and privacy of data. In the field of cyber-security, these are particularly concerning; however, any researcher dealing with confidential participant speech should also be uneasy with third-party access to such data. As a result, researchers, particularly early-career researchers and students, may find themselves with no option other than manual transcription. This article presents a secure and effective alternative, building on prior work published in this journal, to present a method that significantly reduced, by more than half, interview transcription time for the researcher yet maintained security of audio data. It presents a comparison between this method and a fully manual method, drawing on data from 10 interviews conducted as part of my doctoral research. The method presented requires an investment in specific equipment which currently only supports the English language.

show abstract

Assessing and addressing algorithmic bias in practice

Cited by 48 publications

References 4 publications

10 things you should know about algorithmic fairness

10 things you should know about algorithmic fairness

Ethical Questions Raised by AI-Supported Mentoring in Higher Education

Producing ‘good enough’ automated transcripts securely: Extending Bokhove and Downey (2018) to address security concerns

Contact Info

Product

Resources

About