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The rapid integration of artificial intelligence (AI) into business processes has led to the necessity of regulating this technology. Issues of algorithm transparency, data bias, accountability for AI decisions, and adherence to ethical standards have come to the forefront.
Starting from August 2024, the EU will implement the AI Act (AIA) (https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai) - the world's first comprehensive law on AI, establishing rules for its use. Companies, especially those operating in the B2B sector, need to understand the new requirements and prepare for their implementation.
Companies using AI face a number of ethical issues:
Transparency of algorithms - understanding the decision-making logic of AI, especially in sensitive areas such as finance or healthcare.
Problem: Many AI models (especially deep learning) operate like a "black box."
Solution: Implementation of XAI (Explainable AI) (https://www.darpa.mil/research/programs/explainable-artificial-intelligence) - methods that make algorithms interpretable.
Bias and fairness - eliminating discrimination in hiring, lending, and customer service.
Problem: AI can exacerbate discrimination due to bias in the data.
Solution: Auditing datasets and using synthetic data.
Responsibility - who is accountable if AI makes a mistake or takes a harmful decision, especially in high-risk areas (medicine, finance)?
Problem: The human factor at any stage of the development or operation of AI solutions can lead to errors.
Solution: Diligent development, rigorous testing, continuous updating, creation of comprehensive documentation, and ensuring that critical decisions always involve meaningful human oversight.
"Algorithms themselves are not responsible, but the responsibility for their application lies entirely with people" - Margaret Mitchell, AI ethics researcher and one of the authors of the Google AI Principles document.
The AI Act, adopted in the European Union in 2024, became the first comprehensive law regulating the use of AI. It applies not only to companies registered in the EU but also to anyone who brings AI systems to the European market.
"With the AI Act, Europe is sending a clear signal: trust is not a bonus, but the foundation for future technologies" - Thierry Breton, European Commissioner for the Internal Market.
Key points:
Classification of AI systems by risk level:
Unacceptable risk (prohibited): social scoring, real-time biometric identification.
High risk: credit scoring, personnel selection, critical infrastructure, and education require compliance assessment, human control, and risk management.
Limited risk: chatbots, emotion recognition — users must be notified about the use of AI.
Minimal risk: spam filters, AI in video games — minimum requirements.
Transparency: companies are required to inform users if they interact with AI.
Documentation: it is necessary to describe data sources, model logic, and risk mitigation measures.
Post-launch monitoring: mandatory real-time system evaluation.
To meet new requirements and gain trust, businesses need to implement responsible AI principles from the very beginning:
Privacy by Design (https://wecandevelopit.com/news/data-privacy-regulatory-compliance-business-realities-2024-2025) - data protection is embedded in the architecture.
Explainability - the model must be able to explain its actions.
Bias check - regular assessment of the model's fairness.
Sustainability (https://wecandevelopit.com/news/sustainable-development-and-environmental-technologies) - reducing energy consumption and environmental impact.
There are international recommendations and tools that help implement ethical AI. These approaches help create AI systems that are not only effective but also lawful, fair, and trustworthy:
OECD AI Principles (https://www.oecd.org/en/topics/sub-issues/ai-principles.html) and UNESCO Recommendations
NIST AI Risk Management Framework (https://www.nist.gov/itl/ai-risk-management-framework) (USA)
ISO/IEC 42001 (https://webstore.iec.ch/en/publication/90574) - the first international standard for AI management
AI Ethics Checklists from OpenAI (https://openai.com/safety), Google, IBM, etc.
A set of measures to ensure and maintain compliance of the AI product with legislative norms:
Audit of current solutions - risk assessment and compliance of AI systems.
Employee training - preparation of developers, lawyers, and product managers.
AI governance - creating the role of an AI responsible person, implementing governance processes.
Independent assessment - using external tools to verify the transparency and fairness of models.
Dialogue with regulators - participation in the development of standards and practices.
In 2024-2025, issues of AI ethics and regulation are no longer optional but a business necessity. Artificial intelligence can bring significant benefits - but only with a responsible approach.
Following laws such as the AI Act and implementing the principles of AI Ethics by Design allows businesses to minimize risks, build trust, and ensure sustainable development.
The company We Can Develop IT offers:
Audit of AI systems and risks.
Designing solutions in accordance with the requirements of the AI Act.
Assistance in ensuring transparency, privacy, and fairness.
Support in creating documentation and training teams.
Let's create AI solutions that are not only smart but also safe, ethical, and future-ready.
Read also:
The rapid integration of artificial intelligence (AI) into business processes has led to the necessity of regulating this technology. Issues of algorithm transparency, data bias, accountability for AI decisions, and adherence to ethical standards have come to the forefront.
Starting from August 2024, the EU will implement the AI Act (AIA) (https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai) - the world's first comprehensive law on AI, establishing rules for its use. Companies, especially those operating in the B2B sector, need to understand the new requirements and prepare for their implementation.
Companies using AI face a number of ethical issues:
Transparency of algorithms - understanding the decision-making logic of AI, especially in sensitive areas such as finance or healthcare.
Problem: Many AI models (especially deep learning) operate like a "black box."
Solution: Implementation of XAI (Explainable AI) (https://www.darpa.mil/research/programs/explainable-artificial-intelligence) - methods that make algorithms interpretable.
Bias and fairness - eliminating discrimination in hiring, lending, and customer service.
Problem: AI can exacerbate discrimination due to bias in the data.
Solution: Auditing datasets and using synthetic data.
Responsibility - who is accountable if AI makes a mistake or takes a harmful decision, especially in high-risk areas (medicine, finance)?
Problem: The human factor at any stage of the development or operation of AI solutions can lead to errors.
Solution: Diligent development, rigorous testing, continuous updating, creation of comprehensive documentation, and ensuring that critical decisions always involve meaningful human oversight.
"Algorithms themselves are not responsible, but the responsibility for their application lies entirely with people" - Margaret Mitchell, AI ethics researcher and one of the authors of the Google AI Principles document.
The AI Act, adopted in the European Union in 2024, became the first comprehensive law regulating the use of AI. It applies not only to companies registered in the EU but also to anyone who brings AI systems to the European market.
"With the AI Act, Europe is sending a clear signal: trust is not a bonus, but the foundation for future technologies" - Thierry Breton, European Commissioner for the Internal Market.
Key points:
Classification of AI systems by risk level:
Unacceptable risk (prohibited): social scoring, real-time biometric identification.
High risk: credit scoring, personnel selection, critical infrastructure, and education require compliance assessment, human control, and risk management.
Limited risk: chatbots, emotion recognition — users must be notified about the use of AI.
Minimal risk: spam filters, AI in video games — minimum requirements.
Transparency: companies are required to inform users if they interact with AI.
Documentation: it is necessary to describe data sources, model logic, and risk mitigation measures.
Post-launch monitoring: mandatory real-time system evaluation.
To meet new requirements and gain trust, businesses need to implement responsible AI principles from the very beginning:
Privacy by Design (https://wecandevelopit.com/news/data-privacy-regulatory-compliance-business-realities-2024-2025) - data protection is embedded in the architecture.
Explainability - the model must be able to explain its actions.
Bias check - regular assessment of the model's fairness.
Sustainability (https://wecandevelopit.com/news/sustainable-development-and-environmental-technologies) - reducing energy consumption and environmental impact.
There are international recommendations and tools that help implement ethical AI. These approaches help create AI systems that are not only effective but also lawful, fair, and trustworthy:
OECD AI Principles (https://www.oecd.org/en/topics/sub-issues/ai-principles.html) and UNESCO Recommendations
NIST AI Risk Management Framework (https://www.nist.gov/itl/ai-risk-management-framework) (USA)
ISO/IEC 42001 (https://webstore.iec.ch/en/publication/90574) - the first international standard for AI management
AI Ethics Checklists from OpenAI (https://openai.com/safety), Google, IBM, etc.
A set of measures to ensure and maintain compliance of the AI product with legislative norms:
Audit of current solutions - risk assessment and compliance of AI systems.
Employee training - preparation of developers, lawyers, and product managers.
AI governance - creating the role of an AI responsible person, implementing governance processes.
Independent assessment - using external tools to verify the transparency and fairness of models.
Dialogue with regulators - participation in the development of standards and practices.
In 2024-2025, issues of AI ethics and regulation are no longer optional but a business necessity. Artificial intelligence can bring significant benefits - but only with a responsible approach.
Following laws such as the AI Act and implementing the principles of AI Ethics by Design allows businesses to minimize risks, build trust, and ensure sustainable development.
The company We Can Develop IT offers:
Audit of AI systems and risks.
Designing solutions in accordance with the requirements of the AI Act.
Assistance in ensuring transparency, privacy, and fairness.
Support in creating documentation and training teams.
Let's create AI solutions that are not only smart but also safe, ethical, and future-ready.
Read also:
EthicsAndAI
AIRegulation
AIAct2024
ResponsibleAI
ExplainableAI
XAI
AlgorithmTransparency
AICompliance
AIEthicsByDesign
AIPrivacy
DataBias
AIAccountability
TrustworthyAI
AIinBusiness
AIFrameworks
NISTAI
ISOIEC42001
UNESCOAIPrinciples
OECDAIPrinciples
HighRiskAI
AIStandards
AITrust
EthicalAI2024
AIandLaw
AITransparency
AIForGood
AIGovernance
AIinEU
AIResponsibility
BusinessAICompliance
AIinRegulation
AIAdaptation
PrivacyByDesign
SustainableAI
AITraining
AIImpact
AIOversight