Machine Learning in Real-World Scenarios
- Machine learning (ML) is transforming industries, from healthcare to finance, by enabling data-driven decision-making.
- However, its deployment raises significant ethical implications that must be carefully considered.
- An ethical implication is a potential positive or negative consequence that a decision or action may have.
- These consequences can affect various aspects of life, including well-being, justice, fairness, rights, and freedom.
Key Issues
Accountability
Who is responsible when ML systems make mistakes?
- Developers, users, and even the algorithms themselves may share responsibility.
- Though in many cases, the problem that transpires is there is no clear accountability, which is required when life is at risk.
In autonomous vehicles, determining liability in accidents is a complex ethical and legal challenge.
Consent and Privacy
- Given the basis of ML relies on data, it often involves collecting and analyzing personal data
- Therefore, it is vital in obtaining informed consent to protect privacy and autonomy
Privacy is a fundamental right recognized by international human rights declarations.
Algorithmic Fairness
- ML algorithms can perpetuate biases, leading to discriminatory outcomes.
- This problem carries onto possible biases in training data which can lead to unfair and inequitable outcomes.
- A hiring algorithm trained on biased data may favor certain demographics over others
- A medical diagnosis tool trained primarily on data from one demographic may be less accurate for others.
Mitigating these biases will require diverse training data sets, continuous monitoring, and prudent design of the algorithm
Environmental Impact
- Training and even deploying ML models consumes significant energy and resources.
- This requires us to explore minimizing energy consumption and using sustainable approaches or initiatives.
- Training GPT-3 reportedly used several thousand petaflop/s-days of compute.
- This training process consumed hundreds of megawatt-hours (MWh) of electricity.
- Depending on the energy mix (how much comes from fossil fuels), this could emit over 500 metric tons of CO₂ equivalent, the same as:
- Driving a car over 1 million miles.
Societal Impact
- ML and AI can disrupt industries, displace jobs, and alter social interactions as it shifts the way of working in a day-to-day basis.
- Respective measures need to be taken such as mitigating negative impacts and transitioning the workforce to the new environment.
