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Automated Decision Making

• Automated decision making refers to the process of using algorithms and computer systems to make decisions without direct human intervention

• These decisions are based on data analysis, pattern recognition, and predefined rules, enabling computers to rapidly and accurately evaluate vast amounts of information

• Automated decision making has numerous applications across various domains, streamlining processes and improving efficiency

Credit scoring & loan approval

• Financial institutions use automated decision making to assess creditworthiness and determine loan approvals

• Algorithms analyse an individual’s credit history, income, and other relevant data to assess the risk of not making payments and make decisions as to whether or not a person will be granted a loan

Medical diagnosis & treatment recommendations

• In healthcare, automated decision making aids in medical diagnosis and treatment recommendations

• AI systems can analyse patient data, medical history, and symptoms to provide timely and accurate diagnoses and suggest appropriate treatment options

Fraud detection & prevention

• Banks and financial institutions employ automated systems to detect and prevent fraudulent transactions

• Advanced algorithms analyse transaction patterns and user behaviour to flag suspicious activities in real-time

Recommendation systems

• Online platforms, such as e-commerce websites and streaming services, use automated decision making to provide personalised recommendations to users

• Algorithms analyse user behaviour, preferences, and past interactions to suggest products, movies, or content tailored to individual tastes

Traffic management & navigation

• Smart traffic management systems use automated decision making to optimise traffic flow, detect congestion, and adjust traffic signal timings based on real-time data

• Navigation apps use algorithms to provide the most efficient routes to drivers

Manufacturing & supply chain optimisation

• Automated decision making optimises manufacturing processes by adjusting production schedules, inventory levels, and resource allocation based on demand forecasts and real-time data

• Supply chain management systems use algorithms to improve logistics and minimise delivery times

Recruitment & hiring

• Automated decision making is used in recruitment and hiring processes to screen job applicants, assess their skills, and rank candidates based on qualifications and compatibility with job requirements

Automated trading in financial markets

• In financial markets, automated trading systems use algorithms to execute buy and sell orders based on predefined trading strategies

• High-frequency trading relies on rapid automated decision making to capitalise on market fluctuations

Predictive maintenance in manufacturing

• Automated systems analyse sensor data from machines and equipment to predict potential failures and schedule maintenance activities proactively, reducing downtime and optimising maintenance costs

Impact of Automated Decision Making

• The increasing use of computers to make decisions automatically, primarily through Artificial Intelligence (AI) and machine learning algorithms, presents a host of moral, social, ethical, and cultural implications and risks

• These implications impact various stakeholders involved in decision making processes, including:

  • Those who make the decisions

  • The people affected by the decisions

• There is a need for additional information collection to ensure the decisions are accurate and valid

Moral implications

Fairness & bias

• Automated decision making algorithms can maintain biases present in the data used to train them, leading to discriminatory outcomes for specific individuals or groups

• This raises moral concerns about fairness and equal treatment

Accountability & responsibility

• The use of AI for decisions may blur the lines of accountability

• When errors occur, determining responsibility becomes challenging, especially if the decision making process is obscure

Social implications

Transparency & trust

• Lack of transparency in automated decision making processes can erode public trust in the systems

• People may be reluctant to accept decisions made by algorithms without understanding the underlying rationale

Impact on employment

• Automation of decision making in various industries may lead to job displacement, affecting the workforce and raising concerns about economic stability

Access to technology

• Differences in socioeconomic status and access to technology and digital literacy can worsen inequalities in decision making results, which could put some social groups at a disadvantage

Ethical implications

Informed consent

• Decisions made by automated systems can significantly impact individuals’ lives

• Ethical considerations arise regarding obtaining informed consent from affected individuals, especially if they are unaware of the decision making process

Privacy & data collection

• Automated decision making may require additional data collection, which raises ethical questions about privacy and the responsible use of personal information

Algorithmic transparency

• The ethical principle of algorithmic transparency calls for making automated decision making algorithms interpretable and understandable to ensure accountability and prevent hidden biases

Cultural implications

Cultural sensitivity

• Automated decisions may not adequately account for cultural nuances and preferences, potentially leading to decisions that clash with cultural values

Bias in cultural representation

• It is possible for AI algorithms to keep cultural biases and fail to fully represent certain cultural perspectives when making decisions

Need for additional information collection

Data quality & bias mitigation

• To ensure accurate and valid decisions, additional data collection may be necessary to improve data quality and mitigate biases in the training datasets

Validation & accountability

• Additional information may be needed to validate the accuracy and reliability of automated decisions

• Transparency in the decision making process aids in holding algorithms accountable

Feedback loop

• Continuous data collection and feedback loops are essential for evaluating the real-world impact of automated decisions and iteratively improving the algorithms’ performance

Case Study – Amazon’s gender-biased recruitment tool

• Amazon abandoned an artificial intelligence (AI) recruitment tool because it was biased against women

• The AI system was trained on data submitted by applicants over a 10-year period, most of which came from men

• Consequently, the system taught itself that male candidates were preferable

• The tool was designed to review job applications and give candidates a score from one to five stars

• However, by 2015, it was clear that the system was not rating candidates gender-neutrally

• The system began to penalise CVs that included the word “women”

• Although the program was edited to make it neutral to the term, it was eventually deemed unreliable, and the project was abandoned

External link to BBC News article (opens in a new tab)