Topics like “data ethics” and “AI ethics” use to be topics that weren’t really discussed. Nowadays, the world’s largest tech firms are hiring more people to deal with the ethical issues that come from using a lot of data, especially for AI.
Making AI and machine learning ethical: 5 ideas
While there are many applications and benefits of using AI and machine learning in various fields, the ethical questions raised also need to be addressed.
Here are 5 practical ideas on how to make AI and machine learning ethical.
1) Fairness and justice
Employing data and algorithms that do not inflict harm or marginalize individuals based on attributes such as race, gender, age, religion, etc. This involves testing for biases and engaging stakeholders in the design and assessment of AI and machine learning systems.
2) Privacy and consent
Utilizing secure and encrypted data while respecting individuals’ choices and preferences. Keeping people informed about data and AI practices, offering options for opting in or out, and adhering to privacy policies.
3) Explainability and accountability
Utilizing transparent and interpretable data and algorithms, along with documenting and auditing AI and machine learning processes and outcomes. Defining roles and responsibilities for these systems, and providing mechanisms for appeal or error correction.
4) Impact and value
Adopting human-centered and inclusive data and algorithms, and evaluating the social and environmental ramifications of AI and machine learning systems. Supporting and empowering workers and communities affected by these systems to ensure they derive benefits from them.
5) Trust and reliability
Utilizing accurate and consistent data and algorithms to ensure the quality and safety of AI and machine learning systems. Promoting ethical and responsible use, fostering trust and cooperation among individuals, and establishing trustworthiness with AI and machine learning systems.
TML: Texter Machine Learning by Texter Blue
Your content and data are the foundation upon which your business operates, and critical decisions are made. Recent advancements in AI in areas such as image and natural language processing have enabled a whole new level of automatic extraction of information and data analysis that power the automation of key business processes not possible until now.
- Process your data with different AI engines, integrating the results.
- Supports several data formats: images, video, text, etc.
- Generate new content and document versions based on AI results.
- Store extracted information in metadata, enabling further processing and process automation.
- On cloud or on-premises – in case you don’t want data to leave your private infrastructure.
- Compatible with several different ECM providers
- Ability to develop custom AI models to target your specific needs and data.
AI is essential to remain relevant!
The adoption of AI in modern organisations is essential to remain relevant and competitive, optimising efficiency, empowering new business opportunities and freeing critical human resources to specific value-added tasks.
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