Generative AI Challenges Slides | Ethical, Trust, Copyright

Generative AI challenges and threats. See new challenges, expanding challenges and impact on existing challenges by using generative AI. Ethical concerns copy right issu, data quality issue, uncontrolled and unpredictable behavior are few of challenges one need to consider while implementing generative AI.

CHALLENGS OVERVIEW

CHALLENGS OVERVIEW

THREATS

THREATS

TYPE OF CHALLENGES

TYPE OF CHALLENGES

UNCONTROLLED BEHAVIOR

UNCONTROLLED BEHAVIOR

ETHICAL ISSUES

ETHICAL ISSUES

DATA OWNERSHIP

DATA OWNERSHIP


Summary of Generative AI threats, challenges, risks

Dimensions of threats
  • New threats
    •
  • How existing threats are changing
    •
  • how existing threats have expanded
    •

    Ethical challenges
  • Lack of transparency
    •
  • bias
    •
  • Data privacy
    •
  • IP and copyright violations
    •

    Environment challenges
  • high energy, compute
    •
  • Carbaon foot print
    •

    Gen AI Risks and Threat


    Generative AI: Challenges and Ethical Considerations

    Generative Artificial Intelligence (AI) has revolutionized various industries by enabling machines to create content, such as images, text, and music, that mimics human creativity. However, along with its advancements, generative AI also poses several challenges and ethical issues that need to be addressed.

    Challenges Threats Ethical Issues Uncontrolled Behavior Data Ownership Copyright Challenges
    Generative AI faces challenges in ensuring the quality and accuracy of the content it generates. There is a risk of producing misleading or harmful information. One of the major threats of generative AI is the potential misuse of generated content for malicious purposes, such as deepfakes and misinformation. Ethical concerns arise regarding the use of generative AI in creating fake content that can deceive individuals or manipulate public opinion. Uncontrolled behavior of generative AI systems can lead to unintended outputs or biases in the generated content, impacting its reliability. Issues related to data ownership arise when generative AI uses datasets without proper consent or acknowledgment of the original creators. Copyright challenges emerge when generative AI produces content that infringes upon existing intellectual property rights, raising questions about legal responsibility.

    Addressing these challenges and ethical considerations is crucial to harness the potential benefits of generative AI while mitigating its risks. Stakeholders must collaborate to establish guidelines and regulations that promote responsible use of this technology.

    Explainability challenges

    There are a number of challenges in the interpretation of generative AI. These include:

    Lack of transparency: Generative AI models are often complex and opaque, making it difficult to understand how they work. This can make it difficult to interpret the output of these models and to identify potential biases or errors.
    Data bias: Generative AI models are trained on large datasets of data. If these datasets are biased, then the models will also be biased. This can lead to the models generating output that is biased or discriminatory.
    Unintended consequences: Generative AI models can be used to generate a wide variety of output, including text, code, images, and music. It is important to be aware of the potential unintended consequences of using these models. For example, a generative AI model could be used to generate fake news articles or to create deepfakes.
    Despite these challenges, generative AI is a powerful tool that has the potential to be used for a variety of purposes. It is important to be aware of the challenges in the interpretation of generative AI and to take steps to mitigate these challenges.

    Here are some additional tips for interpreting generative AI:

    Understand the model: It is important to understand how the generative AI model works. This will help you to interpret the output of the model and to identify potential biases or errors.
    Be aware of data bias: Generative AI models are trained on large datasets of data. If these datasets are biased, then the models will also be biased. It is important to be aware of the potential for data bias and to take steps to mitigate it.
    Consider the potential unintended consequences: Generative AI models can be used to generate a wide variety of output, including text, code, images, and music. It is important to be aware of the potential unintended consequences of using these models.

    Feedback loop challenges

    Generative AI models are trained on large datasets of data. If this data is not updated regularly, the model can become stale and produce outdated or inaccurate output. This is known as the staleness challenge.

    In addition, generative AI models can be susceptible to feedback loops. This occurs when the model is trained on data that is itself generated by the model. This can lead to the model producing output that is increasingly biased or inaccurate. This is known as the feedback loop challenge.

    To address the staleness challenge, it is important to regularly update the data that is used to train the generative AI model. This can be done by collecting new data or by updating existing data with new information.

    To address the feedback loop challenge, it is important to use a variety of data sources to train the generative AI model. This will help to prevent the model from becoming biased or inaccurate.

    It is also important to monitor the output of the generative AI model for signs of bias or inaccuracy. If any problems are identified, the model can be updated or retrained to address the problems.

    By following these steps, it is possible to mitigate the challenges related to staleness and feedback loops in generative AI.

    Here are some additional tips for mitigating the challenges of staleness and feedback loops in generative AI:

    Use a variety of data sources: When training a generative AI model, it is important to use a variety of data sources. This will help to prevent the model from becoming biased or inaccurate.
    Monitor the output of the model: It is important to monitor the output of the generative AI model for signs of bias or inaccuracy. If any problems are identified, the model can be updated or retrained to address the problems.
    Update the model regularly: It is important to regularly update the generative AI model with new data. This will help to ensure that the model is up-to-date and accurate.

    From the blog

    Build Dataproducts

    How Dataknobs help in building data products

    Enterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product.

    Be Data Centric and well governed

    Generative AI is one of approach to build data product

    Generative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner.

    Well governed data

    Data Lineage and Extensibility

    To build a commercial data product, create a base data product. Then add extension to these data product by adding various types of transformation. However it lead to complexity as you have to manage Data Lineage. Use knobs for lineage and extensibility

    Develop data products with KREATE and AB Experiment

    Develop data products and check user response thru experiment

    As per HBR " Data product require validation of both 1. whether algorithm work 2. whether user like it". Builders of data product need to balance between investing in data-building and experimenting. Our product KREATE focus on building dataset and apps , ABExperiment focus on ab testing. both are designed to meet data product development lifecycle

    Innovate with experiments

    Experiment faster and cheaper with knobs

    In complex problems you have to run hundreds of experiments. Plurality of method require in machine learning is extremely high. With Dataknobs approach, you can experiment thru knobs.

    Why knobs matter

    Knobs are levers using which you manage output

    See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

    Spotlight

    Generative AI slides

    Learn generative AI, LLM, foundation model
  • Learn generative AI - applications, LLM, architecture
  • See best practices for prompt engineering
  • Evaluate whether you should use out of box foundation model, fne tune or use in-context learning
  • Most important - be aware of concerns, issues, challenges, risk of genAI and LLM
  • See vendor comparison - Azure, OpenAI, GCP, Bard, Anthropic. Review framework for cost computation for LLM

    • KREATE

    Adopt generative AI use case without risks

    Our product KREATE can generate web design. Web design that are built to convert

    Using KREATE you can publish marketing blog with ease. See KREATE in action

    Fractional CTO for generative AI and Data Products

    Hire expertise without commitment

    Startup and enterprise who wish to build their own data prodct can hire expertise to build Data product using generative AI

  • Generative AI expertise
  • Machine Learning expertise
  • Data product building expertise
  • Cloud - AWS, GCP,Azure
    •


    Challengs-overview    Data-ownership    Ethical-issues    Genai-challenges-concerns    Threats    Type-of-challenges    Uncontrolled-behavior