What do you do if the reliability and accuracy of new AI technology in your field is in question?

Here are some tenets of a good AI data foundation: 1. Data Provenance– Understand and document where each piece of data originated, how it was collected, and any transformations it underwent. This ensures the data can be trusted for AI training. 2. Dynamic Data Monitoring– Implement systems to continuously monitor data quality and relevance to quickly identify drift in data characteristics or emergence of new biases. 3. Granular Bias Detection– Techniques like subgroup analysis can help understand how different slices of data may affect AI performance disparately. 4. Ethical Data Governance– Develop and enforce a comprehensive ethical data governance framework that ensures data quality, privacy, and fairness.

AI reliability and accuracy are connected with the design of your AI solution and the datasets you used. When you start a new project gather from all the stakeholders their requirements and agree on realistic expectations and outcomes. Investigate the data quality, data integrity and datasets sizes you could use. Understand what is the current state in terms of existing digital infrastructure. Involve in a diverse and inclusive manner all the user groups and communities in both, a bottom-up and top-down approach. Then, start co-developing your AI technology design with them. Finally, allow enough time for evaluation testing, performance testing and then deployment testing to eliminate issues with bias, underfitting or overfitting!

In facing uncertainties regarding the reliability and accuracy of new AI technology in our field, we recognize the need to adopt a rigorous evaluation process. For machine learning applications, we emphasize scrutinizing metrics like R2 and F1 scores to ensure predictive effectiveness. Similarly, in generative AI, understanding the specific use case is essential. We stress the importance of crafting precise prompts and contextual cues, such as RAG, and integrating business context to align technology capabilities with organizational goals.

Evaluating the data used by AI technology is paramount when assessing its reliability and accuracy, especially in fields where trustworthiness is critical. By scrutinizing the data, practitioners can identify potential biases, errors, or limitations that may affect the AI's performance. This process involves examining the source, quality, diversity, and representativeness of the data to ensure it adequately captures the complexities of real-world scenarios. Addressing data deficiencies or biases through pre-processing techniques, data augmentation, or sourcing additional datasets can enhance the AI's robustness and accuracy, instilling confidence in its outcomes and fostering trust among stakeholders.

At Atom360, we prioritized overcoming dataset bias by dedicating substantial time to perfecting our data before training our AI algorithms. We employed rigorous validation and extensive real-world testing to ensure robust performance across all scenarios. Additionally, we established a feedback mechanism to capture user insights and concerns, enhancing the system iteratively. Transparency about the AI's operations was maintained to build trust and ensure informed usage among stakeholders. These steps were crucial for integrating reliable AI technology effectively.

This is one of THE major problems of our times : how do we code AI with the higest standards in human ethics? If we have coding that is biased, then we will have "garbage in garbage out" and that will only compound. With the law of accelerating returns, problems will accelerate fast, IF we do not have the highest Ethical standards across the board in place. Testing for these standards rigorously before implementation must be mission critical.

Focus on rigorous experimentation and testing in real-world scenarios. Begin with controlled experiments to assess the AI's performance under various conditions. Gradually introduce the AI into actual operational environments in a limited capacity to evaluate how it performs in practical applications. Establish a feedback loop to continuously collect and analyze performance data, allowing for quick identification and implementation of necessary improvements. This process of rapid iteration helps refine the technology, enhancing its reliability and accuracy. As confidence in the AI grows, scale its deployment gradually, closely monitoring its effectiveness at each step to ensure its continued suitability and performance.

It's also important to gather feedback. Use insights from users who interact with the AI. Their experiences can provide valuable information on what needs improvement. Always update the systems. As AI learns and evolves, continuous updates can fix initial inaccuracies and adapt to new data, enhancing reliability over time.

I agree. Input bad data and see the resulting output. Then see the links and end result. Answer the question, given a spurious result, having identified the bad input source, what and where is the remedy, at bith ends? Both input source code and output results are erroneous.

It's very important aspect of AI development, especially in fields like EEG signal processing and analysis where accuracy is paramount. ?? Cross-Validation: Use different subsets of your data for training and testing. This helps ensure that the AI model generalizes well to new, unseen data. ?? Validation with Ground Truth: Compare the AI’s outputs with a set of data where the correct answers are already known. This can help determine how well the AI is performing. ?? Peer Review: Have other experts in the field review the AI’s methodology and results. Peer feedback can provide insights that you might have missed.

Lastly, collaborate with others in your field. Sharing knowledge about AI challenges and solutions can lead to better technology and practices, benefiting everyone involved.

One thing I've found helpful is to explain the results with scientific evidence , especially with experts and then try to use explainable AI algorithm and check again with AI experts

In the dynamic landscape of AI technology, consulting seasoned experts is pivotal. Leveraging deep industry insights, these professionals clarify paths through both technical and ethical complexities, establishing a solid foundation for decision-making. Their guidance helps dissect intricate problems, ensuring that the technological solutions we adopt are not only innovative but grounded in practical, real-world applications. This dialogue between innovators and scholars fosters a culture of continuous improvement, ultimately enhancing the reliability and accuracy of AI systems in critical sectors.

When the reliability and accuracy of AI technology are in question, it is prudent to consult with AI ethics committees and relevant regulatory bodies. These groups are equipped with the expertise to assess AI technologies comprehensively, focusing on ethical implications, compliance with standards, and societal impact. Their insights can guide you on necessary improvements or adaptations to ensure the technology is not only effective but also responsibly integrated. This step is especially critical if the AI is intended for sensitive or impactful applications, ensuring adherence to legal and ethical norms.

When the reliability and accuracy of new AI technology in my field are in question, I consult experts and follow a rigorous validation process to ensure the AI system shares accurate information. This includes cross-validation, type of AI project consideration, and continuous feedback loops to monitor performance and make adjustments as needed. By following these steps, I can ensure the AI systems in my field provide reliable results and accurate information.

It is not just the AI but the dynamics of the problems AI solves change frequently. Implementing a CI/CD pipeline helps to improve the results on the go. The system is not hard to set up but the biggest challenge I experience is the validation part. Do not depend on automated tests until you are sure they are effective. I recommend manually deploying the first several generations of models with hands-on testing.

AI is not a 'set it and forget it' tool—it's a living, evolving system that demands continuous attention. In our experience, the difference between AI that thrives and AI that fails often comes down to a commitment to ongoing learning and adaptation. The reality is, the most successful AI systems are those that are regularly fine-tuned and updated. Whether it’s feeding in new data, tweaking algorithms, or even making major adjustments, staying agile is non-negotiable. Think of it as a marathon, not a sprint. The long-term value of AI lies in its ability to adapt and grow alongside your business. And that means never getting too comfortable with where your system stands today.

As an AI expert, I believe it's important to stay updated on the latest advancements and best practices in AI technology. Continuous learning allows professionals to remain informed about potential improvements or updates that could enhance the reliability and accuracy of AI systems in their field.

Research into Online Learning, where models continuously adapt to streaming data, shows promise. Techniques like Dynamic Evaluation and Gradient Episodic Memory enable models to rapidly absorb new information while mitigating catastrophic forgetting. But adaptation introduces risks. Models can drift off course, learning patterns that don't generalize. Rigorous monitoring of live performance is essential. Automated alerting systems should track KPIs and flag anomalies for human intervention. Advances in Continual Learning, like Elastic Weight Consolidation and Progress & Compress, allow models to learn new tasks without overwriting old knowledge.

Continuous monitoring of the AI system's performance is crucial to ensuring its long-term reliability and accuracy. This involves setting up systems to track the AI's effectiveness and detect any decline or deviation in performance. Regular audits and performance reviews help identify areas where the AI may need updates or improvements. This could involve retraining the AI with new datasets to address emerging trends or biases, adjusting algorithms to enhance decision-making processes, or integrating new technologies and methodologies.

To ensure ethical and transparent AI, experts should delve into the technology's explainability. This involves understanding how the AI arrives at its decisions. Here's what they can do: Algorithm Scrutiny: Analyze the AI's underlying algorithms to identify potential biases inherent in their design. Explainable Outputs: Request methods for interpreting the AI's outputs. Can the AI explain its reasoning in a way humans can understand? Data Lineage: Trace the data used to train the AI back to its source. This helps identify potential biases present in the data itself. By focusing on these areas, experts can assess the ethical implications of the AI and ensure transparency in its decision-making process.

When addressing concerns about the reliability and accuracy of AI technology, it is vital to conduct ethical reviews to ensure that the system aligns with ethical standards and does not perpetuate biases or discrimination. This includes examining the AI's decision-making processes and outcomes for fairness and impartiality. Establishing an ethical framework for AI operation and maintaining transparency about how the AI functions and makes decisions can build trust among users and affected parties.

? Pense em um espelho que come?a a distorcer as imagens. Isso n?o seria um espelho fiel, n?o é mesmo? ~ Aqui, as?considera??es éticas?s?o como o polimento desse espelho. é garantir que o espelho reflita de forma justa e precisa, sem distor??es ou preconceitos. ~ Mas, e se o polimento n?o for suficiente? ~ Nesse caso, talvez seja hora de olhar além do espelho. Talvez seja hora de questionar o que está sendo refletido no espelho. Questionar as práticas e os princípios que est?o sendo refletidos. ~ Isso significa ir além da IA e olhar para a cultura e os valores da organiza??o. ? N?o se limite a polir o espelho. Olhe além do espelho e questione o que está sendo refletido! #ai #ia

We should focus on rigorous testing and ensuring a responsible use. This means double checking outputs, making sure the data used to train our model is of high quality, and especially being transparent about what the AI can and cannot do. It's an exciting and constantly changing field, but building trust means being honest about the tech's limitations.

Ethics in AI isn’t just a checkbox—it’s the bedrock of trust in this technology. We’ve seen firsthand how overlooking ethical considerations can derail even the most advanced AI systems. Ensuring your AI aligns with ethical standards isn’t just about avoiding bias or discrimination; it’s about building a system that people can rely on and trust. Transparency is key—without it, even the most sophisticated AI can quickly lose credibility.

? A IA é um livro que você está tentando entender, mas as palavras est?o confusas, as frases n?o fazem sentido e os capítulos parecem desconexos. ? A?‘revis?o de documenta??o’?é como a leitura desse livro. Mas, e se a leitura n?o for suficiente? E se o livro continuar a ser confuso e ininteligível? ? Talvez seja hora de questionar o autor do livro e a inten??o e o propósito do livro. ? N?o se limite a ler o livro. Olhe além do livro e questione o autor. #ai #ia

Although IP professionals are rightfully conservative about using AI due to concerns about data privacy, the IP profession cannot stick the head in the sand. A golden rule for the future ought to lie in exercising uttermost care in assessing data protection clauses in agreements with AI tool providers. Given the sensitive nature of patent-related data, ensuring strict adherence to privacy agreements is paramount to maintain confidentiality. A best practice may involve running AI applications exclusively on an internal network when dealing with sensitive information and sharing no data externally. Doing so can mitigate the risk of unauthorized access or data breaches, safeguarding valuable IP assets and maintaining the trust of clients.

Reviewing the manufacturer’s documentation is essential. It offers insights into the system’s capabilities and limitations, as well as guidance on troubleshooting issues. By studying this documentation, we can understand the system’s constraints, potential pitfalls, and areas that may present challenges. For example, in EEG signal processing, it is crucial to know whether the AI can manage data that is noisy or laden with artifacts.

Reliability and accuracy are metrics that depends on multiple factors : 1. Data quality and it should be taken care in terms of bias, balance for every label, trusted sources … 2. Certain factors highly influence the output. Try out explainability methods to understand which factors are highly influencing and at what rate so that measures can be taken to handle them. 3. Basic algorithms might not always yield accurate models , try ensemble techniques that take advantage from every algorithm to make accurate and reliable models

If the reliability and accuracy of new AI technology are questionable, thorough testing and validation protocols must be implemented. For instance, if a predictive maintenance AI model is doubted for its accuracy, it should undergo extensive validation against historical data and real-world scenarios. Stakeholders may also consider enlisting expert opinions and conducting pilot studies to ensure the technology meets industry standards before widespread deployment.

As well as democratising the hiring process it is also changing the power relationship, historically all held by the employer. Job applicants are using AI now in their job search, how will employers respond to this? Ban it? Limit it? Utilise it themselves? As applications for jobs rise due to job application bots employers no longer hold all the cards when it comes to deciding whether or not to do something…they may be forced into it. Once the UK’s first best practice on AI in Hiring is launched it will get debated again in Parliament.

Improving Training Data: If the data used to train an LLM is inaccurate, misleading, or biased, the model will reflect those flaws. focus should be on on using high-quality sources and techniques to debias data sets Transparency and Explainability: Sometimes it's not clear how an LLM arrives at an answer making it difficult to trust its outputs. AI decision-making processes should be more transparent Human oversight and collaboration: it's important to have humans in the loop when using AI, especially in critical applications. People can identify and correct errors made by AI systems. Focus on continual learning: The world is constantly changing, and AI systems need to adapt. LLMs can be designed to learn and improve over time

One of few things are to be reviewed / considered: 1) have you chosen the best model? 2) is your dataset representative enough of the problem? 3) is your data “clean”? 4) do you have enough data history to capture all possible explanatory variables? 5) have you deeply understood the business problem / use case you are trying to solve / predict?