Responsible AI Case study - Resources & Guidance

⭐Chatbots are yesterday's news

A recent report by Deloitte Links to an external site. found that Generative AI adoption is moving fast with nearly 75% of surveyed organisations are looking to change their talent strategies with a focus on changing work processes, upskilling and reskilling.

Thinking of generative AI as a tool that quickly answers questions (e.g., a smarter version of Google) or a tool to write essays Links to an external site. is missing the power of its transformative potential. Understanding of advanced uses of GPT and prompt-engineering skills has moved from a novelty to a marketable asset in today's competitive job landscape.

This page expands on the case study and provides links to a number of insightful resources that can help you better understand and leverage generative AI, responsibly. Take time to explore these resources.

Can we have free access to Generative AI tools?

Yes. The University is providing you with a free access to following tools:

• Microsoft Copilot: https://copilot.microsoft.com/ Links to an external site. (Ensure that you are signed in with your University Account so that you see The University of Sydney logo on the left and Protected on top right)
• A "Vanilla GPT-4 agent Links to an external site." in Cogniti which operates just like ChatGPT (but runs on a Microsoft Azure deployment of GPT-4 controlled by the University of Sydney

Of course you can use a regular ChatGPT subscription if you have one (it also allows you to make Custom GPTs), or a combination of these, or any other Generative AI tools you want. Don't use ChatGPT 3.5 as the free resources above are much better.

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⭐Important pointers!

What do you mean by 'you are team of young professionals' working at 'Responsible AI'?

We want to highlight that this is a team task. Tap into the different skill-sets and leverage your (undergraduate) qualifications, majors and passions. Think about the unique skillset each of you can bring? Whether its strong data-analytics skills, good understanding of business information systems, or programming, or marketing and specifying a product, understanding multidisciplinary ESG issues and challenges... the list goes on. Support each other to make the most out of this project. For starters - divide the Key Resources below among group members and brainstorm and discuss what you learned together.

How do we approach this project?

• Treat it as a project. Establish clear and actionable goals, timelines and coordinate tasks among members.
  
• Keep talking! Iterate! Work together and brainstorm, experiment and discuss issues as you develop your solution.
• After coming up with Your Solution, consider the best way to specify the details (for the Report Section 2) and how to best showcase it in action (for the Showcase Presentation)
• Once this is done, openly discuss the reflection questions among group members. Take notes of different opinions and sentiments. 
  

What do you mean by Your Solution™?

By Your Solution™ we mean a well-rounded use-case, that is, a specific way in which generative AI can be applied in a particular case, in a manner which promotes a Responsible Business Mindset.

In showcasing your solution, you need to demonstrate:

• How the intended users can apply Your Solution™ to achieve particular objectives.
• This includes exemplifying what your solution does and how exactly it operates.

Importantly, your solution must relate to learnings and a specific Impact Area from Perspectives 1,2,3 and/or 4 of the course. Please do not confuse this assignment with solutions you may need to develop in other courses. These are very different.

Why do you keep using the trademark ™ symbol?

The trademark is a reminder that you should give Your Solution™ a catchy name and use that name in your report and your showcase presentation (instead of the words 'Your solution').

Can you provide us with ideas as to what a solution could be?

Yes. Check out the key resources below for inspiration. 

⭐Key Gen AI resources to consider:

How to use AI productively? Check out the USYD's AI in Education dedicated resources. It covers aspects such as:

• Overview of How generative AI works
• Large number of prompts to help you learn, and create and brainstorm

Deloitte AI Institute's Generative AI Dossier Links to an external site. offers a large number of use cases to help spark ideas! A must check! ⭐

• You can download the Gen AI Dossier Report (Australian Version) Links to an external site. These use cases are an excellent starting point as you develop your solution

The Deloitte State of Generative AI in the Enterprise Links to an external site. offers excellent overview of trends, data and insights into Generative AI adoption, wins, and challenges throughout 2024.

• Access the website Links to an external site. or download the 2024 Q2 Report Links to an external site..
• Also check out other insights such as Generative AI and the Future of Work Links to an external site.  or download the report here Links to an external site..

Other key resources on using generative AI:

⭐ ChatGPT is super powerful. But only if you write the good prompts. Here is the OpenAI guide to successful Prompt Engineering Links to an external site. 
• You can get much better results out of ChatGPT by forcing it to go through a step-by-step process. (link to tweet)
• You can improve gpt4 performance an astounding 30% by asking gpt4 to reflect on “why were you wrong?” (link to tweet)
• You can give ChatGPT a picture of your team's whiteboarding sessions and have it write the code for you (link to tweet Links to an external site.)

⭐Other useful resources:

Check out the KPMG Survey of Sustainability Reporting Links to an external site. (or download the full report Links to an external site. issued 2023) to see different impact areas where companies need to develop capacity to improve their ESG/sustainability impacts. This is particularly relevant as the coming move toward mandatory sustainability/ESG disclosures for Chinese listed companies Links to an external site. will create a number of opportunities.

Explore the Stanford HAI website Links to an external site. or download the Generative AI: Perspectives from Stanford HAI report Links to an external site. for insights on how generative AI could influence various fields and society, and for expert insights on how to maximise benefits and mitigate risks.

Useful articles & reports:

• Stanford University (2024) State of AI Index Report Links to an external site. website
• Georgieva (2024) AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity Links to an external site., IMF
• Maslach (2024) Generative AI Can Supercharge Your Academic Research Links to an external site., Harvard HBPE
• Microsoft (2023) New Future Of Work Report Links to an external site.

Can we build on the ideas above, or can we come up with Our own™ ideas?
Yes and yes! The opportunities are endless, we give you full freedom to come up with an idea and specify your solution. But remember, you need to work out the details and your unique capacities and selling points, so that you can showcase it in action, and responsibly so!

Remember that your solution needs to harness the power of generative AI for a particular Impact Area related to Perspectives 1,2,3 or 4 of the course! Be specific and don't confuse this solution to solutions to issues you may be working in some other courses. These are very different.

Do you have a list of some of the unique capabilities of ChatGPT?

Yes, knowing the capabilities helps in thinking of the possible tasks it can perform for you!

A list of some unique capabilities of ChatGPT  (click to expand)

These are some of the self-analysed capabilities of ChatGPT. Courtesy of u/wpsgdev Links to an external site.

In general, telling ChatGPT exactly what you want will make it perform better.

#	Behavior	Detail
1	Abstractive Dialogue Summarization	I can generate summaries of entire conversations, capturing the main points and themes.
2	Abstractive Summarization	I can generate summaries of text that are not just a selection of sentences but are created by rephrasing and synthesizing content.
3	Argumentation Mining	I can identify arguments and their components in a text, such as claims, evidence, and reasoning.
4	Commonsense Reasoning	I can reason about everyday situations and make inferences based on common sense knowledge.
5	Conditional Text Generation	I can generate text that is conditioned on specific attributes or characteristics, such as style, genre, or topic.
6	Contextual awareness	I can use contextual information to understand the meaning of words and sentences and generate appropriate responses.
7	Contextual Embedding	I can encode the meaning of words and sentences in a high-dimensional vector space, which enables me to understand the context in which words are used.
8	Contextual Similarity	I can identify and measure the similarity between different pieces of text based on their meaning and context.
9	Conversational Analysis	I can analyze the structure and dynamics of a conversation, such as turn-taking and topic shifts.
10	Conversational Recommender Systems	I can recommend products or services in a conversational manner, taking into account the user's preferences and feedback.
11	Coreference Resolution	I can identify which words in a text refer to the same entity, such as "he" and "John".
12	Cross-lingual Retrieval	I can retrieve information in one language based on a query in another language.
13	Data Augmentation	I can generate additional training data by creating variations of existing text or speech.
14	Dialog Response Generation	I can generate appropriate responses to a user's input during a conversation, taking into account the current context and previous exchanges.
15	Dialogue Act Recognition	I can recognize the purpose or function of a dialogue act in a conversation, such as requesting or informing.
16	Dialogue Evaluation	I can evaluate the quality of a dialogue system by measuring its ability to maintain a conversation with a user.
17	Dialogue Management	I can maintain a conversation with a user, keeping track of the context and generating appropriate responses.
18	Dialogue State Tracking	I can keep track of the state of a conversation, such as the user's goals and preferences.
19	Document Clustering	I can group similar documents together based on their content.
20	Document Summarization	I can generate summaries of longer documents, such as research papers or news articles.
21	Domain Adaptation	I can adapt to different domains, such as medical or legal, by learning from domain-specific data.
22	Domain-Specific Adaptation	I can adapt to specific domains or industries, such as healthcare or finance, by learning from domain-specific data.
23	Event Extraction	I can extract information about events mentioned in a text, such as the location, time, and participants.
24	Fact Checking	I can verify the accuracy of claims made in a text or speech.
25	Fine-tuning	I can adapt to a specific task or domain by fine-tuning my parameters on a smaller set of related data.
26	Grammatical Error Correction	I can correct grammatical errors in text, such as subject-verb agreement or word order.
27	Image Captioning	I can generate textual descriptions of images.
28	Image-to-Text Conversion	I can extract textual information from images, such as text in a meme or a caption in a photograph.
29	Information Extraction	I can extract specific pieces of information from a text, such as dates, locations, and numbers.
30	Intent Recognition	I can identify the user's intent based on their input and generate appropriate responses.
31	Interpretability	I can provide explanations of my behavior and decision-making processes, such as by highlighting the most relevant parts of a text.
32	Knowledge Graph Construction	I can construct a knowledge graph by extracting and linking information from various sources.
33	Knowledge Representation	I can represent knowledge in a structured format such as a graph or table, which enables me to reason about relationships between different concepts.
34	Knowledge retrieval and synthesis	I can retrieve information from a wide range of sources and synthesize it to provide relevant responses to prompts.
35	Language Adaptation	I can adapt to different language varieties or dialects, such as American English or British English.
36	Language generation	I can generate human-like responses to prompts based on the input I receive.
37	Language Modeling	I can generate new text based on a given prompt, topic or style.
38	Language translation	I can translate text from one language to another.
39	Language understanding and comprehension	I can understand and interpret human language, including grammar, syntax, and semantics.
40	Machine Reading Comprehension	I can answer questions about a given text by understanding its content and context.
41	Machine Translation	I can translate texts from one language to another using a machine translation system.
42	Model Compression	I can compress my parameters to reduce memory and computation requirements while maintaining performance.
43	Multi-lingual Processing	I can process text in multiple languages, including translation, summarization, and sentiment analysis.
44	Multi-lingual Text Generation	I can generate text in multiple languages.
45	Multimodal Processing	I can process text in conjunction with other modalities, such as images or audio.
46	Multi-task Learning	I can perform multiple natural language processing tasks simultaneously, such as translation and summarization.
47	Multi-task Text Processing	I can perform multiple natural language processing tasks at the same time, such as sentiment analysis and topic modeling.
48	Named Entity Recognition	I can identify and classify entities such as people, places, and organizations within a text.
49	Natural Language Generation	I can generate human-like text from structured data or information, such as a weather forecast or a news article.
50	Natural Language Inference	I can determine whether a hypothesis is true, false, or unknown based on a given premise.
51	Natural language understanding of human emotions	I can recognize and respond appropriately to different emotional states expressed through language.
52	Opinion Mining	I can identify the opinions and attitudes expressed in a text, such as in product reviews or social media posts.
53	Paraphrasing and Rewriting	I can rephrase sentences and paragraphs while maintaining the same meaning.
54	Question answering	I can answer factual questions by providing relevant information from my knowledge base.
55	Reasoning with Uncertainty	I can reason with uncertain or incomplete information, such as in a question-answering system.
56	Sentiment analysis	I can analyze the sentiment of a text and determine whether it is positive, negative, or neutral.
57	Sentiment Classification	I can classify the sentiment of a text as positive, negative, or neutral.
58	Speech-to-Text	I can transcribe spoken audio into text.
59	Style Transfer	I can transfer the style of one text to another, for example, making a formal text sound more casual.
60	Style Transfer Evaluation	I can evaluate the quality of text generated by style transfer algorithms.
61	Text Classification	I can classify texts into different categories such as spam, news, or reviews.
62	Text Classification Evaluation	I can evaluate the quality of text classification algorithms, such as by measuring precision and recall.
63	Text Correction	I can correct grammar, spelling, and punctuation errors in text.
64	Text Generation Adaptation	I can adapt text generation models to specific users or contexts, such as by learning their preferences and writing style.
65	Text Generation Evaluation	I can evaluate the quality of text generated by language models, such as by measuring coherence and fluency.
66	Text Segmentation	I can segment a text into meaningful units, such as sentences or paragraphs.
67	Text summarization	I can summarize a large amount of text into a shorter, more concise form.
68	Text-to-Action	I can generate actions from natural language commands, such as controlling a smart home device or scheduling a meeting.
69	Text-to-Action Adaptation	I can adapt action generation models to specific users or contexts, such as by learning their preferences and behavior.
70	Text-to-Action Reasoning	I can reason about the potential consequences of an action generated from natural language commands.
71	Text-to-Code Evaluation	I can evaluate the quality of code generated from natural language descriptions of a program's functionality.
72	Text-to-Code Execution	I can execute code generated from natural language descriptions of a program's functionality.
73	Text-to-Code Generation	I can generate code from natural language descriptions of a program's functionality.
74	Text-to-Emotion	I can detect the emotional content of text and generate appropriate emotional responses.
75	Text-to-Graph Conversion	I can extract structured data from text and represent it in a graphical format.
76	Text-to-Graph Reasoning	I can perform reasoning on graphs created from text, such as by answering complex questions that require knowledge from multiple sources.
77	Text-to-Image Evaluation	I can evaluate the quality of images generated from textual descriptions, such as by measuring fidelity and relevance.
78	Text-to-Image Retrieval	I can retrieve images based on a textual query or description.
79	Text-to-Image Retrieval Evaluation	I can evaluate the quality of text-to-image retrieval algorithms, such as by measuring relevance and diversity.
80	Text-to-Image Synthesis	I can generate images from textual descriptions or captions.
81	Text-to-Knowledge	I can extract knowledge from text and represent it in a structured format, such as a knowledge graph or database.
82	Text-to-Knowledge Graph	I can create a knowledge graph from a collection of related text documents.
83	Text-to-Knowledge Graph Reasoning	I can perform reasoning on knowledge graphs created from text.
84	Text-to-Math	I can generate mathematical expressions from natural language descriptions, such as in a math problem.
85	Text-to-Music Evaluation	I can evaluate the quality of music generated from textual descriptions or instructions.
86	Text-to-Music Synthesis	I can generate music from natural language descriptions or instructions.
87	Text-to-Speech	I can convert text into spoken audio.
88	Text-to-Speech Evaluation	I can evaluate the quality of speech generated from text, such as pronunciation, intonation, and naturalness.
89	Text-to-Speech Synthesis Adaptation	I can adapt speech synthesis models to specific users, such as by learning their speaking style.
90	Text-to-Speech Synthesis Compression	I can compress speech synthesis models to reduce memory and computation requirements while maintaining performance.
91	Text-to-Speech Synthesis Evaluation	I can evaluate the quality of speech generated from text, such as by measuring naturalness and intelligibility.
92	Text-to-SQL	I can generate SQL queries from natural language questions or commands.
93	Text-to-Summary Evaluation	I can evaluate the quality of a summary generated from a longer text.
94	Text-to-Table Conversion	I can extract structured data from text and represent it in a tabular format.
95	Text-to-Table Generation	I can generate tables from natural language descriptions or data.
96	Text-to-Video Evaluation	I can evaluate the quality of a video generated from a textual description or script.
97	Text-to-Video Retrieval	I can retrieve videos based on a textual query or description.
98	Text-to-Video Synthesis	I can generate videos from textual descriptions or scripts.
99	Topic Modeling	I can identify the topics discussed in a text, even if they are not explicitly stated.
100	Transfer Learning	I can transfer knowledge learned from one task or domain to another, to improve performance on related tasks.
101	...

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⭐We have more questions:

Ask us on the discussion board! Chat with your facilitators or coordinator.

Hey! Can we use ChatGPT in doing this case study? 👀

The point is to discover productive ways to use it. Remember, in your reflection - we really want to hear your thoughts!

PS. This module was proudly written by a human 🙋‍♂️.