<?xml version="1.0"?>
<rss version="2.0"><channel><title>Learn: Learn</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/?d=1</link><description>Learn: Learn</description><language>en</language><item><title>AI in pain assessment: Balancing innovation with patient safety (5 March 2026)</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/ai-in-pain-assessment-balancing-innovation-with-patient-safety-5-march-2026-r14162/</link><description/><guid isPermaLink="false">14162</guid><pubDate>Tue, 10 Mar 2026 12:22:02 +0000</pubDate></item><item><title>Transparency and training: Keys to trusted AI in health care (IHI, 25 September 2025)</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/transparency-and-training-keys-to-trusted-ai-in-health-care-ihi-25-september-2025-r13669/</link><description/><guid isPermaLink="false">13669</guid><pubDate>Mon, 29 Sep 2025 15:07:00 +0000</pubDate></item><item><title>Evaluating gender bias in large language models in long-term care (11 August 2025)</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/evaluating-gender-bias-in-large-language-models-in-long-term-care-11-august-2025-r13463/</link><description><![CDATA[<p>
	In this study, gender-swapped versions were created of long-term care records for 617 older people from a London local authority. Summaries of male and female versions were generated with Llama 3 and Gemma, as well as benchmark models from Meta and Google released in 2019: T5 and BART. Conclusions from this studies findings included:
</p>

<ul>
	<li>
		Llama 3 showed no gender-based differences across any metrics, T5 and BART demonstrated some variation, and the Gemma model exhibited the most significant gender-based disparities.
	</li>
	<li>
		Gemma’s male summaries were generally more negative in sentiment, and certain themes, such as physical health and mental health, were more frequently highlighted for men.
	</li>
	<li>
		The language used by Gemma for men was often more direct, while more euphemistic language was used for women.
	</li>
	<li>
		In the Gemma summaries, women’s health issues appeared less severe than men’s and details of women’s needs were sometimes omitted.
	</li>
	<li>
		While this study provides evidence of gender bias in LLM-generated summaries for long-term care, the findings are based on one specific domain and dataset. Further research is needed to assess whether similar patterns arise in other health and care settings, such as hospitals or mental health, where documentation styles and service models may differ.
	</li>
</ul>
]]></description><guid isPermaLink="false">13463</guid><pubDate>Mon, 11 Aug 2025 08:30:00 +0000</pubDate></item><item><title>The ethics equation: How AI can transform healthcare responsibly (HSJ, 31 January 2025)</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/the-ethics-equation-how-ai-can-transform-healthcare-responsibly-hsj-31-january-2025-r12694/</link><description/><guid isPermaLink="false">12694</guid><pubDate>Fri, 31 Jan 2025 15:49:00 +0000</pubDate></item><item><title>Mitigating clinical algorithmic discrimination (13 December 2024)</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/mitigating-clinical-algorithmic-discrimination-13-december-2024-r12619/</link><description> </description><guid isPermaLink="false">12619</guid><pubDate>Thu, 09 Jan 2025 13:59:07 +0000</pubDate></item><item><title>Measuring fairness preferences is important for artificial intelligence in health care (May 2024)</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/measuring-fairness-preferences-is-important-for-artificial-intelligence-in-health-care-may-2024-r11383/</link><description> </description><guid isPermaLink="false">11383</guid><pubDate>Thu, 25 Apr 2024 15:50:44 +0000</pubDate></item><item><title>Guiding principles to address the impact of algorithm bias on racial and ethnic disparities in health and health care (15 December 2023)</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/guiding-principles-to-address-the-impact-of-algorithm-bias-on-racial-and-ethnic-disparities-in-health-and-health-care-15-december-2023-r10786/</link><description/><guid isPermaLink="false">10786</guid><pubDate>Mon, 15 Jan 2024 16:43:40 +0000</pubDate></item><item><title>Preventing harm from non-conscious bias in medical generative AI (December 2023)</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/preventing-harm-from-non-conscious-bias-in-medical-generative-ai-december-2023-r10663/</link><description/><guid isPermaLink="false">10663</guid><pubDate>Wed, 20 Dec 2023 10:03:33 +0000</pubDate></item><item><title>Leveraging AI to understand care experiences: Insights into physician communication across racial and ethnic groups (8 March 2022)</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/leveraging-ai-to-understand-care-experiences-insights-into-physician-communication-across-racial-and-ethnic-groups-8-march-2022-r9838/</link><description/><guid isPermaLink="false">9838</guid><pubDate>Fri, 21 Jul 2023 16:14:00 +0000</pubDate></item><item><title>Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction (25 April 2022)</title><link>https://www.pslhub.org/learn/digital-health-and-care-service-provision/288_artificial-intelligence/383_policy-impact-regulation-and-workforce/385_ethical-ai-trust-and-transparency/investigating-for-bias-in-healthcare-algorithms-a-sex-stratified-analysis-of-supervised-machine-learning-models-in-liver-disease-prediction-25-april-2022-r7145/</link><description/><guid isPermaLink="false">7145</guid><pubDate>Tue, 05 Jul 2022 10:36:00 +0000</pubDate></item></channel></rss>
