2021_aisb_opacity

There are 8 entries in this file. Use ‘View Page Source’ in the top right hand corner of the page for the raw bibtex. Entries are coloured according to their type:

• Journal Articles
• Books > Chapters
• Proceedings > Proceeding Papers
• Reports > Tech Reports
• Theses
• Standards & Technical Manuals
• Online > Blog Posts & Social Media Threads
• Videos
• Collections > Datasets
• Software > Games
• Legal Cases > Judicial Opinions
• Laws

---

Deep Opacity Undermines Data Protection and Explainable Artificial Intelligence
(21__deep_opacit_under_data_protec_)

---

Explainable AI As Epistemic Representation
(chan21__explain_ai_as_epist_repres_)

Chan, Lok

---

Function and User-Satisfaction In Explainable AI
(creel21__funct_user_satis_in_explain_ai_)

Creel, Kathleen

---

Technical Barriers To The Adoption Of Post-Hoc Explanation Methods For Black Box AI Models
(lee21__techn_barrier_to_adopt_of_)

Lee, Eunjin and Taylor, Harrison and Hiley, Liam and Tomsett, Richard

---

Robot Mindreading and The Problem Of Trust
(páez21__robot_mindr_probl_of_trust_)

Páez, Andrés

---

Beyond Opacity - Epistemic Risks In Machine Learning
(schweer21__beyon_opacit_epist_risks_in_machin_learn_)

Schweer, Julie and Grüne, Paul and Hillerbrand, Rafaela

---

No Explaination Without Inference
(watson21__no_explain_without_infer_)

Watson, David

---

Proceedings Of The 2021 AISB Convention Symposium On Overcoming Opacity In Machine Learning
(zednik21:_proceed_of_aisb_conven_sympos_)

Zednik, Carlos and Boelsen, Hannes

URL: Link

---