AI: Implementing Secure, Trustworthy, Ethical Best Practices

This report delves into the critical best practices for implementing secure, trustworthy, and ethical Artificial Intelligence (AI) systems. It emphasizes the importance of a human-centered design approach, focusing on user experience, clarity, and control. The report outlines the significance of identifying multiple metrics for performance tracking and the need for rigorous data analysis, including checking for errors and ensuring data integrity, especially with sensitive information. It highlights the importance of understanding the limitations of both the dataset and the model, avoiding correlation-based causal inferences, and clearly communicating these limitations to users. Furthermore, the report stresses the need for continuous monitoring and updating of the system after deployment, incorporating real-world performance and user feedback to ensure high-quality work and maintaining security and privacy. The document also discusses the qualitative data coding process, including concept-driven and data-driven coding approaches, and the importance of systematic analysis and preventing coder variance, referencing relevant publications in the field.

Artificial Intelligence
"Exploring best Practices for implementing Secure,
Trustworthy and Ethical Artificial Intelligence."
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Experience Being a Doctoral Student
Being a Doctoral Student is like a dream come true , it feels that I am doing what I had
expected from me to pursue, the opportunity that everyone does not get , I feel privileged. Now
I feel that after some years I will have my dream career and satisfaction in life. Most importantly
now I am getting good stipend being continuing my path of exploring more knowledge, through
this stipend I can now mange my life chores without compromising my hours on other odd jobs
and can focus on studying and being independent. Most importantly my Head is also very
supportive.
Being a PHD student my life and complete process of gaining the knowledge has changed.
Now I have specific research targets set by my head to achieve in specified time. My day usually
begins at research centre by going through the status of current work I have done and comparing
this status with the goals to achieve. I start performing experiments and literature review of the
topics assigned by my head. Then after performing the work, I take the results and found data for
supervision to my head and then according to his feedback and suggestions I continue my further
work.
In this process sometimes I found my -self in a very tough situation when I could not get
the specified results and positive feedback , in this situation certain thoughts come in my mind
like if I will be successful in pursuing PHD like this or not and so on. Then I discuss my
problems with my Research Head, he suggests me new techniques of finding the solution and
continually motivate me.
It is a more learning phase of my life, now along with research study I am learning the
procedure of presenting the research papers and participating in the conferences. Along with this
now I have elite group of scholar friends and respect in society.
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Artificial Intelligence
Exploring best Practices for implementing Secure, Trustworthy and Ethical
Artificial Intelligence.
Artificial Intelligence is one of the most emerging fields in computer science, it has
opened the new opportunity for improving the life of people around the world, from business to
health- care , it is supporting every defiled. With its association of Internet of thing it has opened
the new doors to open in technology. But with adaption of Artificial Intelligence in the various
fields has raised the question for developing the best practices to build the fairness,
interpretability, privacy and security into these systems. Some of the key practices that are
suggested by the researchers and practitioners are as follows-
Using the human centered approach for designing
This is very important as the user experience the system they develop the predictions,
recommendations and decisions. This can be achieved by –
 Designing the features with appropriate display of the built –in-clarity and control is very
important for designing a good user experience.
 Design by considering the augmentation and assistance so that a single answer can satisfy
many questions.
 It is also recommended to model the potential adverse feedback in advance in the design
process.
 Take the suggestion from various users to design various user cases by taking the
feedback throughout the project.
Identify multiple metrics while designing the application
This can be achieved by following these steps-
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 ‘It is suggested by considering the feedback from the users, quantities that help in
tracking the overall performance of the system by considering the status of short term and
long term goals.
 By ensuring that selected metrics are appropriate according to the requirements of the
project or not.
Directly examining the raw data
In artificial Intelligence the well designed trained data is required to have well structured raw
data. Along with this special attention is required to give to the data with sensitive records by
maintaining the principles of ethics and privacy. This can be done by following these steps-
 Analyze the mistakes in the data by checking the missing values, incorrect labels.
 Check whether the data is represented in a way that represents the requirements of user.
 Checking the Training –serving –skew, that determines the difference between the
performance during the potential skews and adjusting the data according to the required
training data.
 Then checking the redundancy of the data in appropriate form.
Understanding the limitation of the dataset and model
It is very important to analyze the limitations of the designed dataset and model. This
can be done by following these steps-
 It is very important that used correlation model does not use the correlation in
casual references.
 The machine learning is highly dependent on the training data set to
communicate the scope and coverage of the training data set so clarifying the
limitations of the model.
 Being clear and communicating the limitation of the model to the user is highly
recommended.
Continue to monitor and updating the system after deployment
 The continues monitoring helps in ensuring that the model takes the real-world
performance and user –feedback and performance of the system to ensure the high
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quality work , this can be achieved by building the roadmap to address the concerned
issues. It is required to concentrate on the long term and short term goals to achieve the
objectives. Before updating the model it is required to concentrate on the affect on the
overall quality of the system. The more focus should be provided on the security and
maintaining the privacy of the system. The mechanism to provide the security, control
and safety is ethically required to consider while designing the system. By considering
the augmentation and assistance so that a single answer can satisfy many questions.
 It is also recommended to model the potential adverse feedback in advance in the design
process.
 Take the suggestion from various users to design various user cases by taking the
feedback throughout the project.
Qualitative Data Coding Process
The coding is considered as a way of indexing or categorizing the text to establish the
framework for the thematic ideas about it. In the qualitative research the coding is considered
very important and it helps in defining the analysis of data to obtain the objectives. In this
approach the consideration of the coding is done through the approach of the concept-driven
coding and data driven . The codes are developed by taking the consideration of the requirement
of the data. The researchers can use the concepts of pre-determined coding schemes for
developing qualitative data coding process. It is also required to study the previous response and
perform the action plan accordingly. It is required to analyze the paragraphs which contain the
similar coding and develop a systematic approach for it. It is highly required to note down the
meaning of the codes and prevent the coder variance in it. It is required to follow the principles
of the quantitative coding approach or priori coding . The Grounded coding refers to the
approach of allowing the notable themes and patterns that emerge in the document itself. Along
with this the priori coding refers to the approach of using the pre-existed framework for doing
the coding work.
Referencing
Richard.E , (2018), Artificial Intelligence , CRC Publication
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Tim.Jones , (2014), Artificial Intelligence –Systematic approach , Jones &Berlet Publication
Christopher.T , (2004), Artificial Intelligence , New Age International publication
Michel.H , (2010), Artificial Intelligence , Marshall Cavendish Publication
Neeta.D , (2008) , Artificial Intelligence , Technical Publication
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