Case Study Assignment: Exploring Ethical Issues in AI Communication
Objective: To analyze and evaluate the ethical implications of AI in communication through a focused case study, integrating concepts and theories discussed in class.
Word Count: 900 words (excluding references)
- Case Selection: Choose one case study or scenario from the provided list (. The case should involve ethical considerations related to AI in communication.
- Case Overview: Provide a concise overview of the selected case, including the relevant background information, key stakeholders, and the AI technology or application involved.
- Ethical Analysis: Apply ethical frameworks, theories, and concepts discussed in class to analyze the ethical issues presented in the case. Use at least three class texts to support your analysis. Consider the ethical dimensions, potential risks, and impacts on various stakeholders involved.
- Evaluation and Reflection: Assess the strengths and weaknesses of the existing ethical frameworks or guidelines that could be applied to the case. Reflect on the complexities and challenges of addressing the identified ethical issues within the context of AI in communication.
- Recommendations: Based on your analysis, propose specific recommendations or guidelines to address the ethical concerns raised in the case. Consider the responsible and ethical use of AI in communication and its implications for society, privacy, bias, transparency, accountability, and other relevant factors.
- Clarity and Structure: Present your case study in a well-organized manner, using clear and concise language. Structure your analysis logically with headings and subheadings, ensuring a coherent flow of ideas. Use proper citations and referencing for all sources, including the two class texts you incorporate.
- Facebook and Cambridge Analytica: This case deals with the ethical issues around data privacy and misuse. Cambridge Analytica, a political consulting firm, acquired data on millions of Facebook users without their consent. This data was used to influence voter opinion during the 2016 U.S. presidential elections.
- YouTube and Content Recommendation Algorithms: YouTube's content recommendation algorithms have been accused of promoting harmful and extremist content. Critics argue that the algorithm prioritizes user engagement over the quality or veracity of content, thereby contributing to misinformation and radicalization.
- Twitter and AI Bias: In 2020, Twitter's image cropping algorithm was accused of racial and gender bias. Despite Twitter's attempts to create a neutral AI, the model seemed to favor people with lighter skin tones and women in its previews.
- Instagram and Mental Health: Instagram's algorithm prioritizes content that gets the most engagement, often promoting harmful trends and potentially negatively impacting users' mental health. This case study allows students to explore ethical questions around user wellbeing, algorithmic responsibility, and the potential need for intervention in content curation.
- Amazon's Rekognition and Racial Bias: Amazon's facial recognition software, Rekognition, has been criticized for its accuracy and biases, particularly against people of color and women. This case study invites examination of the ethical issues involved in the deployment of AI systems, including bias, fairness, and accountability.
- TikTok and User Content Moderation: TikTok's content moderation algorithms have been under scrutiny for their perceived bias, notably for suppressing content from disabled, non-binary, and plus-size creators. This case prompts discussion about bias in AI, platform responsibility, freedom of expression, and the potential for negative social impact.
- Google Photos and Racial Misclassification: In 2015, Google Photos' image recognition software misclassified two African Americans as gorillas, prompting significant backlash. The incident illustrates the potential harm and offense caused by AI errors, and raises issues around oversight and redress mechanisms in AI systems.
- Netflix and Personalization: Netflix's recommendation algorithm personalizes content for its users, impacting what millions of people watch worldwide. However, this personalization has also been critiqued for creating "echo chambers" and influencing viewers' perceptions and choices in potentially manipulative ways. This case study allows for an examination of ethical issues related to personalized content and user autonomy.
- DeepArt and AI-generated Art: DeepArt, an AI-powered tool that transforms photos into artwork in the style of famous artists, raises questions about AI in the creation of art. Concerns include potential copyright infringement, originality, authenticity, and the impact on artists' livelihoods. This case study encourages students to consider the broader implications of AI in creative fields.
- Spotify and Data Privacy: Spotify uses AI to analyze listening habits and curate personalized playlists. While this leads to an enhanced user experience, it also raises questions about data privacy, consent, and the extensive collection and use of personal information. This case study could be used to explore the ethical issues surrounding data privacy and the potential exploitation of personal data in AI-driven personalization.
The case study will be evaluated based on the following criteria:
- Analysis and Critical Thinking
- Depth and insightfulness of ethical analysis.
- Application of relevant ethical theories and concepts from class texts.
- Consideration of diverse perspectives and stakeholders.
- Integration of Class Texts:
- Effective incorporation of at least two class texts to support the analysis.
- Proper citation and referencing of sources.
- Recommendations and Solutions:
- Quality and feasibility of proposed recommendations or guidelines.
- Consideration of the ethical implications and potential impact of the proposed solutions.
- Clarity and Organization:
- Coherent and well-structured presentation of ideas.
- Clear and concise writing style.
- Proper use of headings and subheadings.
- Grammar and Writing Skills:
- Proper grammar, spelling, and punctuation.
- Correct citation format and adherence to academic writing conventions.