Compliance and Auditing in AI
Expert-defined terms from the Advanced Certificate in AI in Regulatory Affairs course at London School of International Business. Free to read, free to share, paired with a globally recognised certification pathway.
Compliance and Auditing in AI Glossary #
Compliance and Auditing in AI Glossary
1 #
AI (Artificial Intelligence)
AI refers to the simulation of human intelligence processes by machines, typical… #
These processes include learning, reasoning, problem-solving, perception, and language understanding.
2 #
Compliance
Compliance in AI refers to adhering to laws, regulations, guidelines, and specif… #
This ensures that AI systems meet ethical standards, privacy requirements, and other legal obligations.
3 #
Auditing
Auditing in AI involves assessing the performance, behavior, and outcomes of art… #
Audits may include reviewing algorithms, data use, decision-making processes, and overall system performance.
4 #
Bias
Bias in AI refers to the unfair or discriminatory treatment of individuals or gr… #
Bias can arise in AI systems due to biased data, flawed algorithms, or improper training methods.
5 #
Data Privacy
Data privacy in AI concerns the protection of personal information and sensitive… #
Compliance with data privacy regulations such as GDPR (General Data Protection Regulation) is essential to ensure the ethical use of AI technologies.
6 #
Explainability
Explainability in AI refers to the ability to understand and interpret the decis… #
Transparent algorithms and clear decision-making processes contribute to the explainability of AI systems.
7 #
Fairness
Fairness in AI involves treating individuals and groups equitably and without bi… #
Ensuring fairness in AI systems requires addressing bias, discrimination, and other ethical concerns.
8 #
Governance
Governance in AI refers to the framework of policies, procedures, and controls t… #
Effective governance ensures compliance with regulations and ethical standards.
9 #
Interpretability
Interpretability in AI relates to the ability to understand and interpret the re… #
Transparent models and explainable decisions contribute to the interpretability of AI technologies.
10 #
Risk Management
Risk management in AI involves identifying, assessing, and mitigating potential… #
Compliance with risk management practices helps minimize the negative impacts of AI technologies.
11 #
Transparency
Transparency in AI refers to openness and clarity in the design, operation, and… #
Transparent AI systems enable stakeholders to understand how decisions are made and why.
12 #
Accountability
Accountability in AI involves taking responsibility for the decisions, actions,… #
Ensuring accountability in AI requires clear roles, responsibilities, and mechanisms for oversight and compliance.
13 #
Algorithmic Bias
Algorithmic bias in AI refers to the unfair or discriminatory outcomes produced… #
Addressing algorithmic bias is crucial for ensuring fairness and equity in AI systems.
14 #
Data Governance
Data governance in AI involves establishing policies, procedures, and controls f… #
Effective data governance ensures data quality, integrity, security, and compliance with regulations.
15 #
Ethical AI
Ethical AI refers to the development, deployment, and use of artificial intellig… #
Ethical AI promotes fairness, transparency, accountability, and respect for human rights.
16 #
Model Explainability
Model explainability in AI relates to the ability to understand and interpret th… #
Explainable models help stakeholders assess the reliability, accuracy, and fairness of AI systems.
17 #
Regulatory Compliance
Regulatory compliance in AI involves meeting the legal requirements, standards,… #
Compliance with regulations ensures the lawful and ethical use of AI systems.
18 #
Bias Mitigation
Bias mitigation in AI involves strategies and techniques for reducing or elimina… #
Mitigating bias helps enhance the fairness, accuracy, and transparency of artificial intelligence systems.
19 #
Compliance Monitoring
Compliance monitoring in AI involves the ongoing assessment and supervision of a… #
Monitoring compliance helps detect and address potential issues proactively.
20 #
Data Security
Data security in AI involves protecting data from unauthorized access, disclosur… #
Compliance with data security measures helps safeguard sensitive information and mitigate cybersecurity risks.
21 #
Governance Framework
Governance framework in AI refers to the structure of policies, processes, and c… #
A robust governance framework promotes accountability, transparency, and compliance.
22 #
Interpretability Analysis
Interpretability analysis in AI involves evaluating the transparency, explainabi… #
Conducting interpretability analysis helps assess the reliability and trustworthiness of AI systems.
23 #
Risk Assessment
Risk assessment in AI involves identifying, analyzing, and prioritizing potentia… #
Conducting risk assessments helps organizations understand and mitigate the risks of AI deployment.
24. Transparent Decision #
making
Transparent decision #
making in AI refers to the process of making decisions that are clear, understandable, and explainable to stakeholders. Transparency in decision-making enhances trust, accountability, and compliance in AI systems.
25 #
Accountability Mechanisms
Accountability mechanisms in AI refer to the processes, controls, and structures… #
Establishing accountability mechanisms promotes ethical behavior and compliance.
26 #
Bias Detection
Bias detection in AI involves identifying and analyzing instances of bias in alg… #
Detecting bias helps organizations understand and address issues of fairness, equity, and transparency in AI systems.
27 #
Compliance Framework
Compliance framework in AI refers to the structure of policies, procedures, and… #
A comprehensive compliance framework promotes lawful and ethical AI practices.
28 #
Data Governance Policies
Data governance policies in AI are the guidelines and rules that govern the coll… #
Effective data governance policies ensure data quality, integrity, privacy, and compliance with regulations.
29 #
Ethical Guidelines
Ethical guidelines in AI are principles, norms, and values that govern the ethic… #
Adhering to ethical guidelines promotes responsible AI practices and ensures respect for human rights.
30 #
Model Transparency
Model transparency in AI refers to the visibility and openness of machine learni… #
Transparent models enhance trust, accountability, and compliance in AI systems.
31 #
Regulatory Requirements
Regulatory requirements in AI are the laws, regulations, standards, and guidelin… #
Compliance with regulatory requirements is essential to ensure legal and ethical AI practices.
32 #
Bias Prevention
Bias prevention in AI involves implementing strategies and controls to prevent b… #
Preventing bias helps organizations build fair, accurate, and transparent AI systems.
33 #
Compliance Reporting
Compliance reporting in AI involves documenting, analyzing, and communicating th… #
Reporting compliance helps demonstrate adherence to regulations and ethical standards.
34 #
Data Privacy Regulations
Data privacy regulations in AI are laws and guidelines that govern the collectio… #
Compliance with data privacy regulations ensures the lawful and ethical use of data in AI technologies.
35 #
Governance Practices
Governance practices in AI are the policies, procedures, and controls that organ… #
Effective governance practices promote accountability, transparency, and compliance in AI.
36 #
Interpretability Assessment
Interpretability assessment in AI involves evaluating the interpretability, tran… #
Conducting interpretability assessments helps organizations understand the decision-making processes and outcomes of AI systems.
37 #
Risk Mitigation Strategies
Risk mitigation strategies in AI are actions and measures taken to reduce or eli… #
Implementing risk mitigation strategies helps organizations manage and minimize the negative impacts of AI deployment.
38 #
Transparent Algorithms
Transparent algorithms in AI are machine learning models and decision #
making processes that are clear, understandable, and explainable to stakeholders. Using transparent algorithms enhances trust, accountability, and compliance in AI systems.
39 #
Accountability Framework
Accountability framework in AI refers to the structure of roles, responsibilitie… #
Establishing an accountability framework promotes ethical behavior and compliance in AI.
40 #
Bias Mitigation Techniques
Bias mitigation techniques in AI are methods and approaches used to address and… #
Applying bias mitigation techniques helps organizations build fair, accurate, and transparent AI systems.
41 #
Compliance Audits
Compliance audits in AI involve assessing and evaluating the adherence of artifi… #
Conducting compliance audits helps organizations identify and address compliance issues proactively.
42 #
Data Security Measures
Data security measures in AI are controls and safeguards implemented to protect… #
Compliance with data security measures helps organizations safeguard sensitive information and mitigate cybersecurity risks.
43 #
Governance Structures
Governance structures in AI are the organizational arrangements, roles, and proc… #
Effective governance structures promote accountability, transparency, and compliance in AI.
44 #
Interpretability Framework
Interpretability framework in AI refers to the structure of methodologies, tools… #
Developing an interpretability framework helps organizations understand the decision-making processes and outcomes of AI systems.
45 #
Risk Management Plan
Risk management plan in AI is a strategic document that outlines the approach, p… #
Developing a risk management plan helps organizations manage and minimize the negative impacts of AI deployment.
46. Transparent Decision #
making Processes
Transparent decision #
making processes in AI refer to the methods and procedures used to make decisions that are clear, understandable, and explainable to stakeholders. Transparency in decision-making processes enhances trust, accountability, and compliance in AI systems.
47 #
Accountability Mechanisms
Accountability mechanisms in AI refer to the processes, controls, and structures… #
Establishing accountability mechanisms promotes ethical behavior and compliance in AI.
48 #
Bias Detection Methods
Bias detection methods in AI are techniques and tools used to identify and analy… #
Detecting bias helps organizations understand and address issues of fairness, equity, and transparency in AI systems.
49 #
Compliance Framework
Compliance framework in AI refers to the structure of policies, procedures, and… #
A comprehensive compliance framework promotes lawful and ethical AI practices.
50 #
Data Governance Policies
Data governance policies in AI are the guidelines and rules that govern the coll… #
Effective data governance policies ensure data quality, integrity, privacy, and compliance with regulations.
51 #
Ethical Guidelines
Ethical guidelines in AI are principles, norms, and values that govern the ethic… #
Adhering to ethical guidelines promotes responsible AI practices and ensures respect for human rights.
52 #
Model Transparency
Model transparency in AI refers to the visibility and openness of machine learni… #
Transparent models enhance trust, accountability, and compliance in AI systems.
53 #
Regulatory Requirements
Regulatory requirements in AI are the laws, regulations, standards, and guidelin… #
Compliance with regulatory requirements is essential to ensure legal and ethical AI practices.
54 #
Bias Prevention
Bias prevention in AI involves implementing strategies and controls to prevent b… #
Preventing bias helps organizations build fair, accurate, and transparent AI systems.
55 #
Compliance Reporting
Compliance reporting in AI involves documenting, analyzing, and communicating th… #
Reporting compliance helps demonstrate adherence to regulations and ethical standards.
56 #
Data Privacy Regulations
Data privacy regulations in AI are laws and guidelines that govern the collectio… #
Compliance with data privacy regulations ensures the lawful and ethical use of data in AI technologies.
57 #
Governance Practices
Governance practices in AI are the policies, procedures, and controls that organ… #
Effective governance practices promote accountability, transparency, and compliance in AI.
58 #
Interpretability Assessment
Interpretability assessment in AI involves evaluating the interpretability, tran… #
Conducting interpretability assessments helps organizations understand the decision-making processes and outcomes of AI systems.
59 #
Risk Mitigation Strategies
Risk mitigation strategies in AI are actions and measures taken to reduce or eli… #
Implementing risk mitigation strategies helps organizations manage and minimize the negative impacts of AI deployment.
60 #
Transparent Algorithms
Transparent algorithms in AI are machine learning models and decision #
making processes that are clear, understandable, and explainable to stakeholders. Using transparent algorithms enhances trust, accountability, and compliance in AI systems.
61 #
Accountability Framework
Accountability framework in AI refers to the structure of roles, responsibilitie… #
Establishing an accountability framework promotes ethical behavior and compliance in AI.
62 #
Bias Mitigation Techniques
Bias mitigation techniques in AI are methods and approaches used to address and… #
Applying bias mitigation techniques helps organizations build fair, accurate, and transparent AI systems.
63 #
Compliance Audits
Compliance audits in AI involve assessing and evaluating the adherence of artifi… #
Conducting compliance audits helps organizations identify and address compliance issues proactively.
64 #
Data Security Measures
Data security measures in AI are controls and safeguards implemented to protect… #
Compliance with data security measures helps organizations safeguard sensitive information and mitigate cybersecurity risks.
65 #
Governance Structures
Governance structures in AI are the organizational arrangements, roles, and proc… #
Effective governance structures promote accountability, transparency, and compliance in AI.
66 #
Interpretability Framework
Interpretability framework in AI refers to the structure of methodologies, tools… #
Developing an interpretability framework helps organizations understand the decision-making processes and outcomes of AI systems.
67 #
Risk Management Plan
Risk management plan in AI is a strategic document that outlines the approach, p… #
Developing a risk management plan helps organizations manage and minimize the negative impacts of AI deployment.
68. Transparent Decision #
making Processes
Transparent decision #
making processes in AI refer to the methods and procedures used to make decisions that are clear, understandable, and explainable to stakeholders. Transparency in decision-making processes enhances trust, accountability, and compliance in AI systems.
69 #
Accountability Mechanisms
Accountability mechanisms in AI refer to the processes, controls, and structures… #
Establishing accountability mechanisms promotes ethical behavior and compliance in AI.
70 #
Bias Detection Methods
Bias detection methods in AI are techniques and tools used to identify and analy… #
Detecting bias helps organizations understand and address issues of fairness, equity, and transparency in AI systems.
71 #
Compliance Framework
Compliance framework in AI refers to the structure of policies, procedures, and… #
A comprehensive compliance framework promotes lawful and ethical AI practices.
72 #
Data Governance Policies
Data governance policies in AI are the guidelines and rules that govern the coll… #
Effective data governance policies ensure data quality, integrity, privacy, and compliance with regulations.
73 #
Ethical Guidelines
Ethical guidelines in AI are principles, norms, and values that govern the ethic… #
Adhering to ethical guidelines promotes responsible AI practices and ensures respect for human rights.
74 #
Model Transparency
Model transparency in AI refers to the visibility and openness of machine learni… #
Transparent models enhance trust, accountability, and compliance in AI systems.
75 #
Regulatory Requirements
Regulatory requirements in AI are the laws, regulations, standards, and guidelin… #
Compliance with regulatory requirements is essential to ensure legal and ethical AI practices.
76 #
Bias Prevention
Bias prevention in AI involves implementing strategies and controls to prevent b… #
Preventing bias helps organizations build fair, accurate, and transparent AI systems.
77 #
Compliance Reporting
Compliance reporting in AI involves documenting, analyzing, and communicating th… #
Reporting compliance helps demonstrate adherence to regulations and ethical standards.
78 #
Data Privacy Regulations
Data privacy regulations in AI are laws and guidelines that govern the collectio… #
Data privacy regulations in AI are laws and guidelines that govern the collection, processing