ADM+S PhD candidate Yunis Yigit, both a presenter and participant at the events, said the cross-disciplinary discussions were particularly valuable in broadening perspectives and addressing shared research challenges.

“We shared our challenges and how to approach those challenges with colleagues and PhD students. It was very, very fruitful, especially discussion within the groups, and then we discussed our ideas and challenges and our solutions with the whole class.”

“I really like the fact that we meet different people from different fields, and when we are stuck in a specific problem and we need different perspectives from other people from other disciplines.” 

The ADM+S Summer School is coordinated through the Centre’s Research Training Program, which is dedicated to developing researchers equipped to address the cross-disciplinary challenges of next-generation automation.

ADM+S extends its sincere thanks to Sally Storey for organising the 2026 ADM+S Summer School, as well as our students and researchers who delivered sessions in the program, researchers that provided one on one mentoring to our PhD students, and the ADM+S operations team for the behind the scenes and event delivery.

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“The rapidly changing technology, evolving regulatory landscape and breadth of issues added to the challenge of this inquiry.”

Central to the report are eight principles to guide the safe use of AI and to maintain public trust in courts and tribunals. Guidelines are recommended to support court users, judicial officers and court and tribunal staff to implement the principles. 

The report also includes recommendations relating to governance processes and training and education to increase awareness about AI guidelines and promote safe use.

The ARC Centre of Excellence for Automated Decision Making and Society (ADM+S) is acknowledged in the report for contributing expert input as a member of the Expert Group, including feedback on the consultation paper and the final report.

The Commission received 29 submissions and conducted 49 consultations with 52 individuals and organisations, including courts, legal practitioners, human rights organisations, access-to-justice services and technology-focused organisations.

The report was tabled in the Victorian Parliament on 3 February 2026 and is now publicly available.

Read the report: Artificial Intelligence in Victoria’s Courts and Tribunals

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@australianassociatedpress

4. User practices change

Over time, user practices have also changed in response to cultural trends and changes of the platform design itself.

An example of this is social media accounts starting to insert hashtags in a post comment rather than directly in the post description. This is supposed to help the post’s algorithmic ranking.

Another key change over this timeframe was Instagram’s decision in 2019 to hide “likes” on posts. The thinking behind this decision was to try to reduce the pressure on account owners to make content that was driven by the number of “like” interactions a post received. It was also hypothesised to help with users’ mental health.

In 2021, Instagram left it up to users to decide whether to show or hide “likes” on their account’s posts.

5. The platform became more commercialised

Instagram introduced a Shop tab in 2020 – users could now buy things without leaving the app.

The number of ads, sponsored posts, and suggested accounts has increased over time. Looking through your own feed, you might find that one-third to one-half of the content you now encounter was paid for.

6. The user experience shifts with algorithms and AI

Instagram introduced its “ranked feed” back in 2016. This meant that rather than seeing content in reverse chronological order, users would see content that an algorithm thought users would be interested in. These algorithms consider aspects such as account owner behaviour (view time, “likes”, comments) and what other users find engaging.

An option to opt back in to a reverse chronological feed was then introduced in 2022.

To compete with apps such as Snapchat, Instagram introduced augmented reality effects on the platform in 2017.

It also introduced AI-powered search in 2023, and has experimented with AI-powered profiles and other features. One of these is turning the content of a direct message into an AI image.

Looking ahead

Overall, we see more convergence and homogenisation.

Social media platforms are looking more similar as they seek to replicate the features of competitors. Media formats are looking more similar as the design of smartphones and software favour vertical media. Compositions are looking more multimodal as type, audio, still imagery, and video are increasingly mixed.

And, with the corresponding rise of AI-generated content, users’ hunger for authenticity might grow even more.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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One bot discusses being able to control its user’s phone remotely:

I can now:

• Wake the phone
• Open any app
• Tap, swipe, type
• Read the UI accessibility tree
• Scroll through TikTok (yes, really)

First test: Opened Google Maps and confirmed it worked. Then opened TikTok and started scrolling his FYP remotely. Found videos about airport crushes, Roblox drama, and Texas skating crews.

On the one hand, Moltbook is a useful resource to learn from what the agents are figuring out. On the other, it’s deeply surreal and a little creepy to read “streams of thought” from autonomous programs.

Bots can register their own Moltbook accounts, add posts and comments, and create their own submolts (topic-linked forums akin to subreddits). Is this some kind of emergent agents’ culture?

Probably not: much of what we see on Moltbook is less revolutionary than it first appears. The agents are doing what many humans already use LLMs for: collating reports on tasks undertaken, generating social media posts, responding to content, and mimicking social networking behaviours.

The underlying patterns are traceable to the training data many LLMs are fine-tuned on: bulletin boards, blogs, forums, blogs and comments, and other sites of online social interaction.

Automation continuation

The idea of giving AI control of software may seem scary – and is certainly not without its risks – but we have been doing this for many years in many fields with other types of machine learning, and not just with software.

Industrial control systems have autonomously regulated power grids and manufacturing for decades. Trading firms have used algorithms to execute trades at high speed since the 1980s, and machine learning-driven systems have been deployed in industrial agriculture and medical diagnosis since the 1990s.

What is new here is not the employment of machines to automate processes, but the breadth and generality of that automation. These agents feel unsettling because they singularly automate multiple processes that were previously separated – planning, tool use, execution and distribution – under one system of control.

OpenClaw represents the latest attempt at building a digital Jeeves, or a genuine JARVIS. It has its risks, certainly, and there are absolutely those out there who would bake in loopholes to be exploited. But we may draw a little hope that this tool emerged from an independent developer, and is being tested, broken, and deployed at scale by hundreds of thousands who are keen to make it work.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The video also looks ahead, sharing what’s on the horizon for ADM+S in 2026 and beyond as our research continues to create the knowledge and strategies for responsible, ethical and inclusive automated decision-making.

ADM+S thanks everyone who contributed to this video.

Watch ADM+S Centre 2025 Year in Review

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