Ethical AI: The DataSum Solution for Scrapped and Unverified Data

November 17, 2023 2 min read By Cogito Tech. 544 views

Ethical use of data in AI systems builds trust and ensures they are adopted and used in a responsible manner.

Data plays a crucial role when it comes to training machine learning algorithms for artificial intelligence (AI). Quality data ensures quality output. The manner in which data is collected, stored, used, and shared impacts individuals, companies, and society.

Implementation of AI solutions imply risk intensification with respect to disparities linked to AI resources, technology, talent, data, as well as computational capacity. This eventually may result in biases that may affect the vulnerable and marginalized communities. In many scenarios, AI might erradicate the human’s ability to subjectively interpret data since machine learning algorithms are made to concentrate single-handedly on variables for enhancing their capability to predict as per training data.

The evolution of AI/ML applications has led to six key ethical data challenges which have to be addressed for development and deployment of AI.

Six Key Ethical Data Challenges

Machine learning algorithms require enormous amount of data to train and optimize AI systems resulting in the need for massive data collection. AI devices enable frequent and hassle-free data collection. The popular use of AI devices has resulted in frequent and effortless data gathering. This data is then linked to other datasets and owing to lack of approval from the original author or authority, it results in copyright infringement.

Biden-Harris Executive Order on AI Data Model and Development

The recent executive order issued by US President Biden sets new standards on AI safety and security, safeguards Americans’ privacy, promotes equity and civil rights, and much more. The executive order casts light on the huge complications and challenges of the AI ecosystem and recognizes the need for actionable insights for navigating this landscape.

Hence, in alignment with the globally recognized principles of transparency, safety, and ethical integrity, the following strategic steps are inspired by Cogito Tech’s DataSum certifications and Fairly AI’s policy library and AI compliance agent, Asenion. These steps can serve as a roadmap for the AI industry.

  1. Ethical AI Algorithms: Implement AI algorithms infused with ethical considerations to guarantee that data-driven decisions uphold safety, privacy, and regulatory compliance. Making ethical AI a foundational pillar will shape our future technological trajectories.
  2. Robust Data Security Measures: Amplify data security measures to protect sensitive information. Employ encryption, strict access controls, and frequent security audits as mandatory components to ensure data integrity and protect user privacy.
  3. Continuous Training and Oversight: Offer exhaustive training on AI systems and consistent oversight by competent professionals. Regular updates and validations of AI algorithms are paramount to mitigate risks and optimize system performance.
  4. Transparent Decision-Making: Cultivate transparency in AI-powered decision-making processes. This not only builds trust among users, practitioners, and stakeholders but also promotes a culture of clarity and accountability, vital in today’s rapidly evolving tech landscape.
  5. Comprehensive Content Authentication: Implement rigorous protocols for verifying and watermarking AI-generated content. By ensuring traceability and authenticity of content, we can combat misinformation, safeguard intellectual property, and further build public trust in AI outputs.

Summing up

Cogito’s DataSum solution ensures your AI models are trained with data that’s ethical, transparent, and free from legal and ethical issues. DataSum ensures unmatched compliance by providing a single, verifiable record that caters to complicated data governance and compliance requirements thereby reducing the risk from copyright infringement.

Data governance is ensured through a transparent perspective with respect to the origin of data, ethical considerations, workforce engagement, thereby establishing a new standard in data accountability. We also ensure your data is trustworthy through our Cogito Tech-verified certifications affirming the dataset’s quality and ethical sourcing.

If you wish to learn more about Cogito’s data annotation services,
please contact our expert.