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MY Critical reviews

These critiques provide insightful evaluations of noteworthy publications within the Human-Computer Interaction (HCI) domain, addressing key questions:

  1. Central Research Idea:

    • Unveils the central research idea within each paper and assesses its level of intrigue and significance.

  2. Research Methods:

    • Examines the employed research methods, gauging their appropriateness and effectiveness.

    • Consideration is given to potential alternative methods that could enhance the study.

  3. Results and Novelty:

    • Scrutinizes the outcomes of the research, appraising their novelty and importance in advancing the HCI field.

  4. Strength Enhancement:

    • Offers constructive insights into potential augmentations for each work, suggesting avenues to fortify their overall impact and scholarly robustness.

Feminist HCI: Taking Stock and Outlining an Agenda for Design

Shaowen Bardzell. 2010. Feminist HCI: taking stock and outlining an agenda for design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1301–1310. https://doi.org/10.1145/1753326.1753521

Learning from and with Menstrupedia: Towards Menstrual Health Education in India

Anupriya Tuli, Shaan Chopra, Neha Kumar, and Pushpendra Singh. 2018. Learning from and with Menstrupedia: Towards Menstrual Health Education in India.Proceedings of the ACM on Human-Computer Interaction 2, CSCW (Nov.2018), 174:1–174:20. https://doi.org/10.1145/3274443

Women’s Safety in Public Spaces: Examining the Efficacy of Panic Buttons in New Delhi

Naveena Karusala and Neha Kumar. 2017. Women’s Safety in Public Spaces: Examining the Efficacy of Panic Buttons in New Delhi. Association for Computing Machinery, New York, NY, USA, 3340–3351. https://doi.org/10.1145/3025453.3025532

Mobile Phones for Maternal Health in Rural India

Neha Kumar and Richard J. Anderson. 2015. Mobile Phones for Maternal Health in Rural India. Association for Computing Machinery, New York, NY, USA, 427–436. https://doi.org/10.1145/2702123.2702258

Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-centered AI Systems

Ben Shneiderman. 2020. Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-Centered AI Systems. ACM Trans. Interact. Intell. Syst. 10, 4, Article 26 (Oct. 2020), 31 pages. https://doi.org/10.1145/3419764

“Everyone wants to do the model work, not the data work” Data Cascades in High-Stakes AI

Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora Aroyo. 2021. Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI.

Re-imagining Algorithmic Fairness in India and Beyond

Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Tulsee Doshi, and Vinodkumar Prabhakaran. 2021. Re-imagining Algorithmic Fairness in India and Beyond. ​arXiv preprint arXiv:2101.09995.​

Human-AI Collaboration in Data Science:
Exploring Data Scientists’ Perceptions of Automated AI

Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. ​Proceedings of the ACM on Human-Computer Interaction​ 3, CSCW: 1–24. ​https://doi.org/10.1145/3359313

Questioning the AI:
Informing Design Practices for Explainable AI User Experiences

Q.V.Liao,D.Gruen,andS.Miller,‘QuestioningtheAI:InformingDesignPracticesfor Explainable AI User Experiences’, in ​Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems​, New York, NY, USA, Apr. 2020, pp. 1–15, doi: 10.1145/3313831.3376590.

Researching AI Legibility through Design

J. Lindley, H. A. Akmal, F. Pilling, and P. Coulton, ‘Researching AI Legibility through Design’, in ​Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems,​ New York, NY, USA, Apr.2020, pp.1–13, doi: 10.1145/3313831.3376792.

Guidelines for Human-AI Interaction

S. Amershi​etal et al.​, ‘GuidelinesforHuman-AIInteraction’, in​Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems,​ New York, NY, USA, May 2019, pp. 1–13, doi: 10.1145/3290605.3300233.

Woman-Centered Design through Humanity, Activism, and Inclusion

Teresa Almeida, Madeline Balaam, and Rob Comber. 2020. Woman-Centered Design through Humanity, Activism, and Inclusion. ​ACM Transactions on Computer-Human Interaction​ 27, 4: 27:1-27:30. https://doi.org/10.1145/3397176

The “Comadre” Project: An Asset-Based Design Approach to Connecting Low-Income Latinx Families to Out-of-School Learning Opportunities

Alexander Cho, Roxana G. Herrera, Luis Chaidez, and Adilene Uriostegui. 2019. The “Comadre” Project: An Asset-Based Design Approach to Connecting Low-Income Latinx Families to Out-of-School Learning Opportunities. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19), 1–14. https://doi.org/10.1145/3290605.3300837

Design-Based Implementation Research: An Emerging Model for Transforming the Relationship of Research and Practice

Barry J Fishman, William R Penuel, Anna-Ruth Allen, Britte Haugan Cheng, and Nora Sabelli. 2013. Design-Based Implementation Research: An Emerging Model for Transforming the Relationship of Research and Practice. 21.

Design Within a Patriarchal Society:
Opportunities and Challenges in Designing for Rural Women in Bangladesh

Sharifa Sultana, François Guimbretière, Phoebe Sengers, and Nicola Dell. 2018. Design Within a Patriarchal Society: Opportunities and Challenges in Designing for Rural Women in Bangladesh. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18), 1–13. https://doi.org/10.1145/3173574.3174110

Intersectional HCI: Engaging Identity through Gender, Race, and Class

Ari Schlesinger, W. Keith Edwards, and Rebecca E. Grinter. 2017. Intersectional HCI: Engaging Identity through Gender, Race, and Class. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ’17), 5412–5427. https://doi.org/10.1145/3025453.3025766

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