Introduction
The case study speculates how a drawing done through artificial intelligence is only attributed to the last creator, yet many provenances were involved in creation. At the same time, they were analyzing a 2017 robot-assisted drawing, which took 4 hours to complete. Various people, including robots and AI, contributed to the completion of the artifact, yet only one person was credited. According to Doyle and Senske (2018), one person credited modified code from the workshop, executed it with the robot and picked the output for the exhibition.
The drawing workshop used Turtle drawing by running the Arduino software. Several people were also involved, including an author who wrote the Arduino Integrated Development Environment (IDE) code, another person for modifying the code, one who wrote a Sierpińskitriangle, and those who moved the robot for programming. This report analyzes the case study and suggests the right course of action to attribute all authors involved in creating the drawing. The problem of failing to credit all authors of the digital drawing can be solved by developing an attribution protocol.
SWOT
Strengths
- The ability to create a robotic drawing.
- Availability of tools and manpower for drawing.
- The accessibility of artificial intelligence (AI).
- Accessible workshop for robotic drawing.
- Programming knowledge or high-quality experts for using AI.
Weaknesses
- Crediting one author for the artifact.
- Lack of proper procedure to credit all involved provenances.
- The challenge of building an attribution network-difficulties combining human and machine work.
Opportunities
- Developing the attribution protocol for all authors.
- Investigating the role of the robot in authorship.
Threats
- Potential lack of authorship recognition by international laws.
- Collaborative efforts could be individualized, causing chaos and conflicts.
- Lack of feedback loop between humans and technology due to the absence of collaborative records.
Technology
- Availability of Turtle robots for drawing.
- AI and other coding and programming technologies.
Major Issues
The primary issue is that despite the contribution of robots, AI, and humans, the 2017 drawing was only credited to the last author. Multiple provenances were involved in the process of completing the painting. Some were programmers, designers, Turtle robots, AI, robot movers, and assemblers. Although the choices made by the last creator of the artifact and the creative process make an act of authorship, the robot and original code were equally essential in the process and output.
The second problem is the lack of an attribution protocol that considers human and robotic labor. The last artifact author was credited with the authorship role because there is no public protocol for crediting such collaboration.
The last challenge is developing an attribution network following the inability to interpret and combine human and robotic labor. According to Doyle and Senske (2018), the problem with provenance in robot-created artifacts or any other object is that creative and intellectual attribution is attached to the item and not only to the machine or software that created it.
Establishing intellectual property of robot-based objects is challenging when digital creation is brought to the tangible form. Digital identifiers and metadata attached to the concerned software do not transmit to a physical object. Furthermore, existing legal systems, including patents, copyrights, trademarks, and design protection, only address the end physical object. These challenges make it impossible to credit all provenances as the case study authors would like.
Alternative Course of Action
Establishment of Attribution Protocol or Network
The first option amongst many is to establish a protocol to associate all contributors to the drawing process with the final artifact. This process would mean crediting both robotic, AI, and human labor to the intellectual property of the artifact. It would also investigate robotic contribution to authorship and legal development of machine-human ownership. In this case, the Turtle robot, AI involved in coding and creation, and all humans involved in the process would be mentioned by name. This recognition would be legal and officially recorded in all exhibition materials or legal systems.
Offer Attribution to the Humans Only
The second option is to credit human labor while ignoring AI’s digital processes and the robot’s contribution. This process would prove easy because legal systems are designed to credit human authorship in artifacts. In this case, movers of the robots, assemblers, programmers, and designers would be credited by their names. The credit would give them intellectual property over the drawing, thus allowing them to have an opinion about its use.
Attribute the Drawing to the Turtle
There is also the option of ignoring all the human labor and crediting the Turtle. According to Doyle and Senske (2018), the Turtle is a robot with a history in education and art designed to teach children programming and procedural thinking. The robot has a tail and head and uses simple instructions to move on the surface and two wheels, thus leaving trails of markers behind.
In other words, the Turtle uses its tail to draw the artifact on the surface. Although this process involves human labor, the workshop team could decide to give all the credit to the Turtle, thus giving up their attribution rights. This option is applicable but not very practical in the real world because the robot may lack a way to use the intellectual rights of the paint. Therefore, the Turtle would be attributed to the drawing, but the workshop would indirectly own it.
Approve Credit for the Last Author
The last option or course of action would be to leave the attribution of the drawing as it is. The logic for this choice would be based on why the workshop team left the credit for the last author. The most possible explanation is that crediting the last author would reduce the number of people owning the paint.
Another possible explanation is that the credited person owned the project. Similarly to manufacturing a product, the production process entails many people and machines, but the last product is attributed to the company. In other words, the many people and technologies who contributed to the process of drawing this artifact are the property of the workshop and were only helping the project’s owner. All the human labor was compensated in this case, but the AI and Turtle were not. Henceforth, all the provenance is the property of the workshop, owned by the credited author, and so is the last product.
Recommended Course of Action
The most suitable course of action is establishing an attribution protocol that accommodates all provenances. This solution is highly reasonable because it resolves the primary issue of the case study. It is only fair to credit all the authors of the drawing because, that way, their efforts are respected and valued. Establishing a legally acceptable attribution network would entail overcoming two critical challenges. The first issue is that robotically created objects have their attribution and intellectual property embedded into the physical object, not the machine or software that generated it.
This problem can be solved by readily available solutions, including digital watermarking, blockchain, and steganography. Metadata, which describes the properties of the artifact, can be embedded to describe the relationship between the fabrication data to the general creation process (Wang, 2021). The fabrication data reveals who, how, why, and what contributed to the creation of the drawing (Doyle & Senske, 2018). In this space, the AI, machines, and people involved will be fully acknowledged for their physical contributions and intellectual rights (Doyle & Senske, 2018). Although produced using AI an algorithm, this solution will make it possible for the drawing to be recognizable under the international laws of authorships and representable in future technology histories.
The second issue that must be solved to develop attribution protocol efficiently is developing intellectual property rights when digital creations become tangible. Unfortunately, digital identifiers, such as the metadata embedded in the concerned data, do not transfer to tangible objects (Doyle & Senske, 2018). The existing legal systems, such as patents and copyrights, are only for physical objects and not digital processes.
The solution here would be finding a way to transfer digital data to the object legally. According to Wang (2021), there are legal constructs that connect digital and tangible provenance. For example, radio-frequency identification (RFID) has tags that offer a reasonable strategy for bridging physical and digital aspects. The RFID tags are electronically banked and work through electromagnetic fields to promptly select and monitor the artifact.
However, as this approach relies upon the RFID being applied to the artifact, it results in the end user’s commitment to ethical attribution instead of directly attaching the authors to the art. Therefore, to conquer the ethical limitation, new technologies could be established to enable metadata to automatically connect to the artifact as part of the generation process (Wang, 2021). For example, a unique marker mark could be used to inscribe attributions to the artifacts, which could be read by machine vision to link the authors to the painting. As scanning technology and fabrication of digital art advance into minimal scales, it will soon be possible to physically attach digital information to the artifact (Wang, 2021). In the meantime, the application of RFID will provide the most viable solution to the problem.
Conclusion
The primary issue of leaving out all drawing authors can be solved by developing an attribution network. The designers of this network would have first to solve two attribution-related issues. Connecting the metadata to the artifact will make it possible to accredit all the contributing provenances.
Acknowledging all the provenance involved in the process of paint fabrication is essential to recognize all the effort. Crediting authorship gives the provenance of intellectual property rights. There are many contributors to this drawing, and although that could be a disadvantage during the use of the drawing, it is only fitting to include all of them in the credit list. The recommended solution outweighs others because it provides the most viable and reasonable action. It also resolves the speculations of the case study authors, thus meeting the purpose of this report.
References
Doyle, S., & Senske, N. (2018). Digital provenance and material metadata: Attribution and co-authorship in the age of artificial intelligence. International Journal of Architectural Computing, 16(4), 271-280. Web.
Wang, B. (2021). Digital design of smart museum based on artificial intelligence. Mobile Information Systems, 1-13. Web.