AI Ethics and Accountability

Study Notes

AI and Bias

Programmers can transfer their own biases and values into AI systems, consciously or unconsciously
AI often relies on historical data, which can be biased due to prevailing cultural or social norms
AI can perpetuate social inequalities, for example, by using algorithms that disproportionately flag minority groups as fraudulent
AI can exhibit ethnocentric bias, for example, by automatically flagging non-English authors as plagiarizing

Lack of Transparency and Accountability

Many AI systems are "black boxes", meaning their inner workings are hidden and their decision-making processes are not fully understood
The lack of transparency makes it difficult to determine who is responsible for AI-related decisions and actions
The complexity of AI systems and the involvement of multiple stakeholders further complicates accountability issues

Invasion of Privacy

AI relies on large amounts of personal data, which can lead to privacy violations
Examples include data breaches, unauthorized access to sensitive information, and extensive surveillance
China's use of facial recognition technology for surveillance is a prominent example of privacy invasion

Ownership and Authenticity

The question of ownership and authenticity of AI outputs is complex
Who owns the content generated by AI? The programmer, the AI company, or somebody else?
How do we determine the authorship of AI-generated content?
Currently, there is no definitive answer to questions regarding copyright and authorship of AI-generated content
AI systems collect and combine styles and images from various sources, making it difficult to assign clear copyright ownership
Despite the lack of clear attribution, some AI outputs may closely resemble the work of specific authors, raising ethical concerns.

Media System Models

Hallin and Mancini outlined three distinct models of media systems in democratic countries: Polarized Pluralist, Democratic Corporatist, and Liberal.

Polarized Pluralist Model

Countries: France, Greece, Italy, Portugal, Spain
High political parallelism, meaning media organizations are often aligned with specific political parties.
Significant state intervention in media regulation and subsidies.
Lower journalistic professionalism, with partisan journalism common.
Limited media market with lower print circulation and a strong tradition of elite-oriented media.

Democratic Corporatist Model

Countries: Austria, Belgium, Denmark, Finland, Germany, Netherlands, Norway, Sweden, Switzerland.
Moderate to high political parallelism, but declining.
High state intervention and high journalistic professionalism.
Media market is robust with high newspaper circulation and a well-developed market for both print and broadcast media.
Strong tradition of public service broadcasting and media subsidies, with an emphasis on maintaining media independence.

Liberal Model

Countries: Canada, Ireland, United Kingdom, United States.
Low political parallelism with limited state intervention.
High journalistic professionalism and a highly commercialized media market.
The UK is an exception with a stronger tradition of partisan news, unlike the other countries in this model.

Key Dimensions of Comparison

Developmental arc of a mass circulation press: This refers to the historical trajectory of mass media development in each country.
Political parallelism: This describes the degree of linkage between media and political parties.
Journalistic professionalism: This examines the norms and practices associated with professional journalism in each country, such as ethics and autonomy.
State role: This refers to the extent of state intervention in shaping the media system.

Description

This quiz explores critical issues surrounding artificial intelligence, including bias, transparency, and privacy concerns. It addresses how biases can be embedded within AI systems and the implications of these biases on society. Participants will also learn about the challenges of accountability in the context of complex AI algorithms.