Compliance and Ethical Considerations in AI Deployment 

Compliance and Ethical Considerations in AI Deployment 

Compliance and Ethical Considerations in AI Deployment 

In recent years, the proliferation of Artificial Intelligence (AI) technologies has revolutionised various industries, offering unprecedented opportunities for efficiency, innovation, and growth. However, the benefits have been accompanied by profound challenges, when it comes to compliance and ethics.  

As businesses increasingly integrate AI into their operations, it becomes imperative to sail the complex landscape of regulatory requirements and ethical considerations to ensure responsible and sustainable deployment. 

Australia’s AI Ethics Principles 

The AI Ethics Principles are voluntary, designed to complement existing AI regulations and practices rather than replace them. By adhering to these principles, businesses and governments can demonstrate their commitment to ethical AI practices, fostering public trust and confidence. 

1) Human, Societal, and Environmental well-being:

AI systems should benefit individuals, society, and the environment, promoting positive outcomes for all. 

2) Human-Centered Values:

AI systems should respect human rights, diversity, and individual autonomy, aligning with human values and promoting equity. 

3) Fairness:

AI systems should be inclusive and accessible, avoiding unfair discrimination against individuals or groups. 

4) Privacy Protection and Security:

AI systems should uphold privacy rights, and data protection, and ensure the security of data throughout their lifecycle. 

5) Reliability and Safety:

AI systems should reliably operate according to their intended purpose, ensuring safety and minimising risks. 

6) Transparency and Explainability:

There should be transparency and responsible disclosure regarding AI systems' processes and impacts, enabling users to understand and contest outcomes. 

7) Contestability:

Individuals impacted by AI systems should have a timely process to challenge their use or outcomes, ensuring accountability and redress for harm. 

8) Accountability:

Those responsible for AI systems throughout their lifecycle should be identifiable and accountable for their outcomes, enabling effective oversight and human control. 

Implementing the AI Ethics Principles enables businesses to build trust in their products and organisations. This trust, in turn, cultivates consumer loyalty to AI-enabled services, driving positive outcomes and ensuring that all Australians benefit from transformative AI technologies. 

 Role of Compliance and Ethical Considerations in AI Deployment 

1) Understanding Regulatory Compliance  

Compliance with existing regulations and standards is crucial for businesses deploying AI technologies. In Australia, organisations must adhere to laws such as the Privacy Act, which governs the collection, use, and disclosure of personal information, and the Australian Human Rights Commission Act, which prohibits discrimination based on factors such as race, gender, and disability. Additionally, industries like healthcare and finance may have sector-specific regulations that govern the use of AI systems. 

2) Addressing Data Privacy and Security  

AI systems rely heavily on data, often requiring access to large volumes of sensitive information. Ensuring data privacy and security is paramount to compliance and ethical AI deployment. Businesses must implement robust data protection measures, including encryption, access controls, and data anonymisation, to safeguard against unauthorised access and breaches. Furthermore, organisations must obtain informed consent from individuals before collecting and processing their data, in line with privacy regulations. 

3) Mitigating Bias and Discrimination  

AI algorithms can be biased, leading to unfair outcomes and reinforcing societal inequalities. To address this, businesses need to thoroughly test and validate AI systems, include diverse perspectives in development teams, and continuously monitor and audit AI applications to detect and correct bias.  

4) Ensuring Transparency and Explainability  

Transparency and explainability are critical for building trust in AI systems and fostering accountability. Businesses should strive to make AI decision-making processes transparent and understandable to stakeholders, including end-users, regulators, and the general public. This may involve providing clear explanations of how AI algorithms work, disclosing data sources and processing methods, and documenting decision-making processes. Transparency also facilitates the identification and mitigation of potential ethical concerns. 

5) Promoting Ethical AI Governance  

Establishing robust governance frameworks is essential for promoting ethical AI deployment within organisations. This includes appointing dedicated AI ethics committees or advisory boards responsible for overseeing AI projects, conducting ethical risk assessments, and developing guidelines and policies for AI development and deployment. Furthermore, fostering a culture of ethical awareness and accountability among employees is crucial for upholding ethical standards across the organisation. 

Proposed Reforms by Australia’s Law Council 

To address the challenges posed by AI, the Law Council of Australia proposes several key reforms: 

1) Establishment of Interdepartmental Taskforce 

A dedicated task force would provide technical advice, monitor international developments, facilitate collaboration, and coordinate AI regulation efforts across state and federal agencies. 

2) Regulation of High-Risk AI Technology 

The Australian Government should focus on regulating high-risk AI applications, such as the use of biometric data and social scoring practices. Enhanced oversight is essential to prevent misuse and protect individuals' rights. 

3) Transparent and Reviewable Automated Decision Making:

Comprehensive regulatory reform is necessary to ensure transparency and accountability in automated decision-making processes, particularly in government operations.   

Future Requirement - A Tailored Approach 

While international regulatory models offer valuable insights, Australia must develop a bespoke regulatory framework that aligns with its unique constitutional and regulatory landscape. By carefully assessing different regulatory approaches, Australia can tailor regulations to suit local needs and market dynamics. 

As AI continues to reshape our world, it's imperative to strike a balance between innovation and ethics. By implementing proactive regulatory reforms and fostering transparency, Australia can harness the transformative potential of AI while safeguarding the interests of its citizens. 

Join us as we help businesses sail the complex compliance landscape and help implement strategies to overcome setbacks and leverage the benefits of AI. 

Risk Management In Manufacturing And Why Choose A Software Solution?

Risk Management In Manufacturing And Why Choose A Software Solution?

The manufacturing industry in Australia is large and complex. This makes it difficult to manage the risks faced by the various departments and locations, alongside keeping up to date with legislation that companies need to stay compliant with. Hence, Risk Management in manufacturing is essential

Risks may emerge due to the nature of the employed materials, equipment, employees, etc. It is essential to have a program that identifies the various risks that can occur, and by extension, their likelihood of occurrence and potential impact on the business, staff, shareholders, customers, and suppliers should a risk be realised.

Thus, for the manufacturing process, there is a need for comprehensive risk identification, risk assessment, risk measurement, risk minimisation, and risk monitoring programs, regardless of the reason for wanting to understand and control risks. This program will need to encompass all business functions to ensure that all key areas of risk concern are accounted for. Typically when companies are attempting to obtain ISO certification, these factors are considered.

Objectives of Risk Management in Manufacturing

  • To minimise injury to staff, customers, and/or the local community and by extension, the financial impact on shareholders and long-term damage on business reputation. Therefore, identifying and controlling the risks that could impact the organisation's reputation is monitored to minimise the likelihood of such risks being realised.
  • To achieve a competitive edge through the development of risk management practices that enable businesses to produce higher-quality goods.
  • To aid manufacturers in gaining the most from an enterprise-wide perspective on risk management requires a more analytical and adaptable approach.
  • To encourage safe work practices and increase awareness with the company culture.

Types of Risk in Manufacturing

There are various types of risks that are encountered in the manufacturing industry, including:

  • Supply chain interruptions
  • Workplace safety
  • Product liabilities
  • Errors and omissions
  • Cargo in transit
  • Cyber risks
  • Employee fraud and injuries
  • Equipment failures
  • Evolving workforce dynamics
  • Product recalls

Risk Management Plan

Creating a robust Risk Management Plan is the first step for many manufacturing organisations when implementing a risk program.

A risk management plan is instrumental to create an organised road map that promotes objectivity in risk identification and prevents the omission of essential risk elements. The plan identifies the individuals involved and is accountable for the efficient implementation of the risk management process.

The plan addresses the need for risk management process management reviews and specifies how and when evaluations will be conducted. If the plan pertains to a particular product, it addresses the entire product lifecycle, from design through production and post-production use (i.e., used by the end customer). Similarly, the risk plan for a manufacturing process or manufacturing organisation's output encompasses the full scope of responsibility and impact throughout the process or organisation. In the plan, the criteria for risk acceptability are specified and a description of how the implementation and effectiveness of any required risk controls are validated. Furthermore, the plan details how information is continuously gathered and fed back into the risk analysis process.

Risk Management Process Implementation

There is a constant need to enquire to implement the risk process.

The Risk Management team continually ask the following questions:

  • What dangers might there be?
  • Which ones are the worst?
  • What are the underlying causes of the risks?
  • Which risks are most likely to materialise?

The risk management team, comprising a group of people with a variety of skills and competencies, will also contribute to the risk process. After the risk team identifies potential risks, they determine potential responses to each risk.

These measures are agreed upon and carried out.

After the implementation of the risk management process, the success of the action taken is assessed

Employees who are responsible for managing risks need to continuously monitor the efficacy of the actions taken and update the risk plan to account for any changes to products or processes and the resulting risk levels.

Measuring the Manufacturing Risk Level

For each aspect of risk, a method for identifying and measuring the risk levels, whether they are financial, customer-related, regulatory, etc. is established.

As you progress through the risk assessment process, you will identify the list of potential risks and used a risk tool to determine the potential severity, probability, and detectability of each potential risk. Each of these aspects are combined to characterise the risk.

One method for combining these various measures is to create a single number as follows:

Severity * Probability * Detect-ability (S*P*D) = Risk Level Number (or a Risk Prioritization Number – RPN).

Once the Risk Prioritization Number is determined, the acceptability of risk is defined and prioritisation of potential risks to be reduced is be agreed upon.

Manufacturers should consider the following in elevating the value of risk assessments:

  • Incorporate risk identification into the process of strategic planning.
  • Innovation and other potential disruptors of strategy should be investigated.
  • Determine mitigation and/or monitoring strategies for the risks with the highest priority.
  • Prioritize mitigation strategies focused on behaviour modification.
  • Define the ownership of key risk mitigation strategies and enforce accountability for results.
  • Consider how to track changes to the strategic plan's assumptions.
  • Define risk indicators and determine information availability.
  • Using both internal and external data to provide objective benchmarks for monitoring key assumptions and strategic risks eliminates bias.
  • Focus conversation on continuous improvement to anticipate a shifting risk environment.
  • Make strategic risk a regular topic of discussion with the board and senior leadership.

Responsibilities of Senior Management

Senior management in a manufacturing organisation ensures that there are resources available, qualified people are assigned to the risk process, the risk acceptability policy is specified and documented, and effective management reviews of the risk process are carried out at regular intervals.

Risk Management Software

As you can see, there are many things to consider when managing risk in your organisation. To support the processes and responsibilities, it is recommended to look at software solutions that make this easier for you to keep track of.

Lahebo is a one-stop solution for all Company’s risk and compliance needs. It has been developed based on 20 years of its sister company, Anitech, working with Australian manufacturers supporting their management systems.

Lahebo supports internal procedures and controls for quality and regulatory compliance, ever-evolving supply chain risks, and integrated risk management practices. It allows you to bring the whole organisation together to work towards zero harm.

Why does your Manufacturing Company need Risk Management Software?

Supporting management systems and optimising manufacturing quality today is not as simple as achieving quality raw materials, increasing training, or recruiting more skilled employees.

Improved risk assessment provides greater agility and protection against disruptive and potentially catastrophic events that characterize prolonged periods of decline. A manufacturer becomes more agile to recognize and respond to such events and capitalize on the opportunities such events reveal.

Internal procedures and controls for quality and regulatory compliance, ever-evolving supply chain risks, and integrated risk management practices are significant quality drivers.

You can save time and money by optimising these by using software, like Lahebo that supports all the factors required to achieve the Objectives of Risk Management in Manufacturing.

If you wish to take care of your Risk and Compliance issues and boost your business, do reach out to us for a demo.

Top Features To Consider When Selecting a Risk Management Software  

Top Features To Consider When Selecting a Risk Management Software  

Risk Management software is a must-have tool for Australian businesses to identify, monitor and evaluate the key risks in their business that affect successful operations. There are many risk management solutions available in Australia but choosing the right one is instrumental for the success of your project and business irrespective of your industry.

A governance, risk and compliance software helps manage data flow and access control within an organisation. Businesses implement GRC platforms to identify risks, enforce governance and policies, data safety, and track compliance.

A sound GRC (Governance, Risk and Compliance) strategy gives clients and organisations an integrated view of their enterprise essential to improving performance. Their key focus would be to overcome risks that can hinder the progress of their business. It helps you to create and manage regulatory and internal compliance measures thus improving business quality and providing learning opportunities.

A GRC software can be on-premises, or cloud-based, with zero coding that allows you to have complete control over your company's activities related to risk and compliance, which increases internal efficiencies.

Here are some of the key capabilities to take into consideration when reviewing and selecting risk management software:

1. Risk Assessment Capabilities

An efficient risk assessment software shall have all the risk assessment capabilities including a facility to report and register risks to the business, a facility to monitor mitigation actions, and a reminder to assess risks in a stipulated amount of time. A key function to seek is the option to have visibility across all departments and locations and to be able to review information at an overall organisational level versus individual departments and locations.

2. Managing Actions

Identifying risks to the organisation is one step. What do you do with the subsequent actions related to the identified risks? Managing the controls and actions to mitigate the risk is a key benefit of risk management software. Having a central platform where risks and their actions can be monitored and evaluated collaboratively decreases the likelihood of disruption to an organisation.

3. Organisation-wide Risk Reporting

Can you define and record different key risk indicators (KRIs) using the risk management software you want to use? For internal reporting with important stakeholders in your organisation, the risk management programme needs to be properly defined and monitored. There should be proper planning done.

With reliable risk management software, you can produce clear, organised, and detailed reports that not only provide review points but also helps you form better decisions for your business and the services provided.

4. Real-time Notifications

Real-time notifications and automated alerting features are a must-have with a risk management solution. Getting caught up in the day-to-day operations of a business means that often the risk register and required mitigation get reviewed only on a periodic basis. Having a solution that provides you and your teams (users) with reminders and updates according to due dates and actions taken means your data is always current.

5. Compliance Management

While keeping in mind the health, safety, and well-being of the people who work there, your organization's risk management procedures must also adhere to all local regulatory requirements. It can be cumbersome to continually monitor websites such as AUSTLII or the Federal Legislative Requirements. Having a real-time automated notification of updates to specific regulations applicable to the successful running of your business will save you time and resources.

6. Completely Auditable Process

Transparency and accountability are essential for many organisational processes. Risk management is undoubtedly one such area where accountability is required. The software programme you want for risk management must be auditable.

This is necessary not only to maintain compliance with regulatory bodies but also in the case of internal and external audits. With fully auditable risk management software, the audit time will become much shorter and less time intensive for teams and users.

For regular use of your risk management software, a clear, user-friendly dashboard is crucial. However, this does not imply that it must be minimal and devoid of essential features. Businesses invest a sizable portion of their product development budget in UI/UX for a reason. Even if the software is excellent, it won't succeed if the user interface is difficult to use or overly complicated, for instance.

Our soon-to-be-launched risk management software Lahebo can provide you with all the top features to overcome risks and enhance your business.

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The Internet of Things in Industry

The Internet of Things in Industry

Manufacturing industry has been looking at automation as a means of boosting quality and productivity for many years, from the days of Henry Ford inventing the production line to the increasing use of robots and the IoT.

Today’s critical industries encompass both the infrastructure and manufacturing sectors.  A new suite of low-cost energy-efficient devices, accessible through WiFi provides the ability to link with Cloud-based applications such as Big Data analytics.  

Some commentators refer to the new era as Industrial IoT (“IIoT”) or Industry 4.0.  Adding IIoT to the mix brings a whole host of new opportunities to continue the process.

On the downside, the relative newness of Industry 4.0 brings new risks as it is deployed. Increased state surveillance, increased criminal activity and supply chain risks follow on from incomplete, missing, or defective cybersecurity included in the new device’s firmware and software.  It clearly needs a stable power and communications infrastructure to operate successfully.

Having accepted all that, and that we need to have robust backup systems to keep the IoT devices running, what are the areas that will benefit from the IIoT?

A Brief Overview of IIoT

IIoT uses much of the same technologies as the broader IoT.   At the end of 2019, there were around 27Billion IoT devices, and over 30Billion are expected in 2022.    The IIoT market is expected to have reached $200Billion in 2021.

IIoT includes the pre-existing Systems Control and Data Acquisition (“SCADA”)  and Industrial Control Systems (“ICS”) systems that are in operation in industrial control and management environments and infrastructures.

IoT brings them together with the objective of enhancing efficiencies and optimising production in manufacturing and the wider delivery of products and services. There will also be benefits in safety improvements and cost reductions.   Many ERP systems can now use data supplied by IoT devices to track and analyse the real-time production process, monitor the condition of manufacturing equipment and provide input to predictive analytics. 

It has also provided new network infrastructures. In the early days, all IoT transactions were sent to core systems for analysis and response.  This generated large volumes of network traffic that could reduce service levels in other applications.

After some thought and research, it was realised that many trans actin were ignored by the core processes.  Moving the analysis functions to the edge of the network would significantly reduce the network traffic, saving cost and improving service levels.  Cloud technologies gave the opportunity to do this, and so, the concept of “Fog Computing” was born, in essence, having many semi-independent network clouds at the network edge.   Transactions were processed in the local cloud, and only the summary transactions needed for overall monitoring were passed back to the core systems.

A new breed of IIoT devices has recently come to the fore,  autonomous transportation.  Just like driverless cars, factories can now use driverless vehicles to transport work-in-progress and finished goods between production steps and finished goods warehouses.   The difference between the prior automated transport systems and the latest driverless vehicles is that the new vehicles are not limited to pre-determined routes laid out as tramlines.

What’s Next?

What is to come rather dep[ends on the continuing development of the hyper-connected Internet environment promised by recent advances in 5G, WiFi and fibre technologies.   Some countries are rolling out smart cities with ubiquitous WiFi coverage and Fibre to the Home.    As infrastructure developments continue to roll out, the ability for Industry to connect factories, suppliers and customers will improve.

Large amounts of data that need to be processed by advanced analytic software will travel on these new superhighways and will need to be met by significant processing and storage capacity.  The growing adoption of Cloud Computing will enhance the process.

A new factor that has emerged over the last two years, following restrictions imposed by the pandemic is the increased use of remote working, both from a mobile perspective and from the new working from home paradigm.

This will change how industry operates, particularly in the service industries, and will build on the infrastructure improvements currently underway.   As an example, 5G, despite its health risks and WiFi communications will allow seamless broadband communications from areas currently underserviced or not serviced at all.

Artificial Intelligence

Strictly speaking, Ai is not part of the IoT, though it will leverage the benefits flowing from the adoption of IIoT in the workplace.   The significant amounts of raw data generated by IIoT can be processed by an Ai engine to increase understanding of the data and the information hidden in it.  For example, it is already starting to be used in the mining and petrochemical industries to analyse survey results and indicate where minerals or oil could be found.

In general terms, AI, linked with IIoT can be used in machine learning to allow individual IIoT devices to improve, alert and on occasion decide how best to operate.


IIoT Is here to stay in industry, in both the manufacturing and service sectors.  The benefits that accrue from being able to process, and with AI, analyse large amounts of raw data can mean the difference between a cost-effective and a redundant process.

To be sure, there are significant cybersecurity issues to be addressed and overcome, but experience shows that is a struggle between the black hats and white hats that will continue.  This time the difference is that failure can have very serious consequences.

Overall though,  industry is embracing IIoT.