It seems to lack a bit of in-depth solutions as many of the solutions are general and although they give an idea of solving the problem it still requires further research

Uses real-world examples to support conclusions and strategies

Authors used 45 sources to get an idea of the main ethical concerns regarding AI in Healthcare

Ai and digital tools are in greater request

Excluded AI development over covid

Issue 7: Beneficence * Act with the best interest of others, promoting welfare and well-being in healthcare * possibility of deception

Strategies:

Improve communication by exhibiting caring behavior and informing patients about their best interests. Encourage AI developers to design friendliness Issue 8: Responsibility * Responsibility for decisions made by AI systems in healthcare * Unclear accountability for AI-related patient harm

Strategies: * Define clear guidelines for ethical and legal decision-making based on AI outputs. * Require both doctors and AI developers to follow the "do no harm" standard. * Involve AI developers and engineers in moral accountability assessments.

Issue 9: Solidarity * Concerns about justice and equality in healthcare due to Ai * Inequality in the distribution of resources and potential discrimination

Strategies: * Establish a solidarity-based model for applying AI solutions in society. * Consider interpersonal justice in the design of care bots to decrease inequality. * Foster communication, trust, and empathy in patient–doctor relationships.

Issue 10: Sustainability * development, deployment, and implementation of AI in healthcare * conflicting goals, unequal contexts, risk and uncertainty, opportunity cost

Strategies: * Support the establishment of trustworthy global AI through shared rules and cooperation. * Develop sustainable ML decision-making tools within occupational healthcare. * Implement a systematic approach to establish digital care

Issue 11: Dignity * respect for human rights and freedoms * Concerns about human dignity through AI models

Strategies: * Design and operate AI systems considerate of human dignity. * Focus on dignity and privacy to respect human rights.

Issue 12: Conflicts * AI implementation results in conflicting goals, and decision-making disparities * Conflicts between patients, medical staff, AI models,

Strategies: * Share decision-making with patients to ensure autonomy * Seek human perspectives through surveys

Issue 6: Trust * trust between humans and AI systems in healthcare * Factors affecting trust include data usage, data-driven technology, data confidentiality, and bias in AI systems

Strategies: * Inform patients * Improve data privacy and confidentiality to maintain patient trust. * Educate healthcare personnel on AI to establish trust in AI healthcare providers.

Issue 5: Patient Safety and Cyber Security

Sub-issue 1: Patient Safety * Unnecessary or potential harm caused by AI tools * Difficulty in assigning responsibility for harm caused by AI Sub-issue 2: Cyber Security * data security and the possibility of hacking

Strategies: * Develop AI with input from clinicians and computer scientists. * review AI tools through legally selected regulatory committees. * Continuously update regulations, codes of conduct, and standards. * Anticipate problems and take proper action for cyber security.

Strategy for Transparency * Make AI capable of explaining its solutions and choices to patients

Issue 4: Transparency: * Lack of transparency due to black box nature of ML * lack of explainability due to black box nature so credibility and reasoning of AI is in question

Strategies for Privacy * Create strict rules and codes for data access and security * Secure data transfer and storage * Optimize patient consent * Incorporate legal rules and healthcare practices

Concern 3: Privacy * 20 sources of 45 * data privacy: control of individual's health info * confidentiality: maintaining privacy of data

Strategies for Addressing Freedom and Autonomy * Ensure humans are in control of decisions * Allow clinicians control of the tech * Universal code for patient-clinician relationship * Make AI Info comprehensible to patients

Ethical Issue 2: Freedom and Autonomy * 22 of 45 sources * sub-issues of control, respect and informed consent * Control (12 sources): The inability to control AI decisions is a concern * Respecting Human Autonomy (9 sources): AI doesn't have the ability to respect a patient's choice instead, it ignores it * Informed Consent (9 sources): AI must be able to obtain consent while respecting privacy and sensitivity

Strategy for Addressing Ethical Issues of Justice and * Understand the difference between training and input data * Evaluate ecological validity of algorithms * Validate algorithms for different groups (ethnic, socio-economic)

Justice and Fairness were the most common ethical issues in the 45 sources (24/25 sources).

Included sources: * English language * Between January 1, 2010 and September 6, 2020 * Used Sources with 10+ citations * Specific to healthcare * Addresses ethical issues

Criteria to Assess Sources: 1. Does the adopted research method address the research questions? 2. Does the study have a clear research objective? 3. Does the study have a specific description of each ethical issue? 4. Does the study have a specific description of strategies related to the ethical issue? 5. Do the results of the study add value to the area of research?

Used 5 databases to obtain info ACM, PubMed, Nature-SCI, IEEE Xplore, and the AI Ethics Guidelines Global Inventory

Uses a Systematic literature review, which involves looking at primary sources concerning the topic

This paper looks at what the ethical issues are in healthcare for AI and strategies to address these issues

Academic publications discussing the ethical issues concerning AI in healthcare do exist, such as “The ethics of AI in health care: A mapping review” by Morley’s research group [41], “Ethical and legal challenges of artificial intelligence-driven healthcare” by Gerke’s group [42], and “A governance model for the application of AI in healthcare” by Reddy’s group [43]. Morley’s group focused on mapping the ethical issues based on epistemic, normative, and overarching perspectives [41]. Gerke’s group explored ethical issues from the perspective of legal challenges, but did not present a systematic review of how AI can influence them in healthcare applications [42]. Reddy’s group addressed the introduction and implementation of a proposed governance model in healthcare. However, AI 2023, 4 30 their specification of the ethical issues only focused on the general governance model for ethical issues related to the essential elements of safety and the responsible use of AI

“The global landscape of AI ethics guidelines” by Jobin’s group, which presents an overview of existing ethical guidelines and strategies [38]; “The Ethics of AI Ethics: An Evaluation of Guidelines” by Hangendorff, which analyzes 22 ethical guidelines for AI, and providing recommendations for overcoming their relative ineffectiveness [39]; and “Artificial intelligence ethics guidelines for developers and users: clarifying their content and normative implications” by Ryan and Carsten Stahl, which provides a elaborative explanation of 11 normative implications of current AI ethical guidelines directed to AI developers and organizational users [40]. Although these three documents present very useful discussions of ethical AI issues in a general domain, none of them specifically address the ethics of AI in healthcare

European Union is a leader in creating a framework for ethical regulations and rules for AI using a "human-centered" approach

Examples of rules and regulations are the “Ethics Guidelines for Trustworthy AI” from the European Commission [33], “Report on the Future of Artificial Intelligence” from the US [34], and the “Beijing AI Principles” from the Chinese government [35].

AI requires explainability and transparency to build trust with the government, the media, legal community, etc

Adoption of AI is limited by: * Accuracy * Explainability * Transparency * Compatibility

AI with ML algorithms that use DL and other techniques leads to black box models. * black-box models arrive at conclusions without providing an explanation * Black Box nature of ML typically clashes with laws * Can lead to consequences for stakeholders if a bad decision is made

Ethical decision-making by AI evaluates social, ethical, and legal requirements however, acceptance and development of AI depends of AI complying with the law, regulations, and privacy principles.

AI is promising in healthcare as it can detect conditions like cancer faster and more accurately.

Machine Learning (ML) now allows AI to solve problems without specific programming. Deep Learning (DL) is a subset of ML can now be used to solve unstructured data problems similar to the human brain.

AI Uses: * Healthcare * Transportation * Education

Addresses 12 main ethical issues and 19 sub-issues

Source follows PRISMA Guidelines: * PRISMA stands for Preferred Reporting Items for Systematic Reviews and Meta-Analysis * 27 item checklist used to improve transparency in Reviews

Challenges: current regulatory challenges in AI development for medicine lack specific regulations - less rigorous testing for medical AI technologies - delayed ethical considerations during clinical trials.

• It emphasizes the importance of timely assessments and notes that existing regulations, like the EU's General Data Protection Regulation and the Artificial Intelligence Act, may not fully address these issues.

• The solution proposed is the embedded ethics approach, seen as adept at filling regulatory gaps by integrating ethical considerations early in AI development.

• The approach aligns with interdisciplinary education trends, capitalizing on corporate willingness to engage in ethical practices and offering a swift response to the growing need for ethical considerations, particularly in medical AI development.

Set differs from plan in that set repre- sents a limiting and narrowing of response alternatives with no inherent process to shift alternative

Set: - predetermined tendencies - habitual way of reacting - through instruction or conscious

Plans, as was mentioned earlier, include criteria to determine successful goal-attainment and, as well, include "feedback" processes-ways to incorporate and use in- formation gained from "tests" of potential solutions against desired goals

Rules are usually "smaller," more discrete cognitive capabilities; plans can become quite large and complex, composed of a series of ordered al- gorithms, heuristics, and further planning "sub-routines.

• A plan is a process that controls the order of operations
• fundamental plan in Humans = TOTE
• T (test): matches possible solutions
• O (operate): proceeds if solution is sensible
• T (post operation test): compares solution with goal
• E (exit): if goals are reached

Heuristic rules: - doesn't guarantee an optimal solution - offers solutions that are good enough

Algorithms: - precise rykes - result in a specific answer to a specific problem -

Rules are: - inferred capability that allows someone to respond to a stimulus or class of stimuli with performance - can be learned directly or inferred from experiences - rules are the primary factor for organizing and intellectual functioning

There is a processing period in problem solving in which a solution is reached and a sense of completion is gained after much contemplation.

In The Experiment. writers were video taped and asked about their rules, plans and beliefs during their writing to examine the composing process without interfering

• Experiment involved 1-3 interviews with each student.
• Experiemnt is more clinical than scientific Led to the following revelation:
• composing is a complex problem-solving process
• disruptions of the process can be explained by the cognitive psychology framework

The difference observed between blockers and non-blockers was the rules they set for themselves. - blockers had rigid impeding strategies - non blockers used less strict rules<br /> - non blockers were more functional, flexible and open to outside info

1) Exploitation in Labor market by Companies 2) Consumer Exploitation in the housing and Financial market

The American Welfare State is a leaky Bucket - Welfare used to use all its funds to provide single-parent families with cash assistance - When Clinton reformed welfare in 1996, he replaced it with TANF, which is a block grant giving states leeway on how to use the money - states don't spend the money properly and instead states use it for other means - Arizona spends welfare for sex ed - Pennsylvania used TANF funds for ant--abortion crisis pregnancy centers - Maine used the money for a Christian summer camp - For every dollar towards TANF in 2020 poor families received 22 cents

Freshmen also often think about their views and identity for the first time.

There is a difference between people who see writing as a means of expressing and learning vs those who see it only as a means to good grades

Writing is a transaction that allows you to express yourself and your beliefs to an audience

when writing find a greater purpose than just a grade, try to get something out of it

The audience for writing is increasingly multilingual and multinational, so writers may need to provide additional information. to explain their ideas

Writers need to embed contextual clues and redundancies to help readers understand

Matsuda's "Myth of Linguistic Homogeneity" says - writers or writing teachers assume that all writers share the same knowledge of language structures and functions - sociolinguistic context affects writing style, etc.